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AI-Lecture-Notes

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"Look at you, hacker: a pathetic creature of meat and bone, panting and sweating as you run through my corridors. How can you challenge a perfect, immortal machine?" ― Ken Levine

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<table class="vertical-navbox nowraplinks">
<tbody>
<tr>
<th><a title="Outline of artificial intelligence" href="https://en.wikipedia.org/wiki/Outline_of_artificial_intelligence">Artificial intelligence</a></th>
</tr>
<tr>
<th><a title="Artificial intelligence" href="https://en.wikipedia.org/wiki/Artificial_intelligence#Goals">Major goals</a></th>
</tr>
<tr>
<td class="plainlist">
<ul>
<li><a title="Knowledge representation and reasoning" href="https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning">Knowledge reasoning</a></li>
<li><a title="Automated planning and scheduling" href="https://en.wikipedia.org/wiki/Automated_planning_and_scheduling">Planning</a></li>
<li><a title="Machine learning" href="https://en.wikipedia.org/wiki/Machine_learning">Machine learning</a></li>
<li><a title="Natural language processing" href="https://en.wikipedia.org/wiki/Natural_language_processing">Natural language processing</a></li>
<li><a title="Computer vision" href="https://en.wikipedia.org/wiki/Computer_vision">Computer vision</a></li>
<li><a title="Robotics" href="https://en.wikipedia.org/wiki/Robotics">Robotics</a></li>
<li><a title="Artificial general intelligence" href="https://en.wikipedia.org/wiki/Artificial_general_intelligence">Artificial general intelligence</a></li>
</ul>
</td>
</tr>
<tr>
<th>Approaches</th>
</tr>
<tr>
<td class="plainlist">
<ul>
<li><a title="Symbolic artificial intelligence" href="https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence">Symbolic</a></li>
<li><a title="Deep learning" href="https://en.wikipedia.org/wiki/Deep_learning">Deep learning</a></li>
<li><a title="Bayesian network" href="https://en.wikipedia.org/wiki/Bayesian_network">Bayesian networks</a></li>
<li><a title="Evolutionary algorithm" href="https://en.wikipedia.org/wiki/Evolutionary_algorithm">Evolutionary algorithms</a></li>
</ul>
</td>
</tr>
<tr>
<th><a title="Philosophy of artificial intelligence" href="https://en.wikipedia.org/wiki/Philosophy_of_artificial_intelligence">Philosophy</a></th>
</tr>
<tr>
<td class="plainlist">
<ul>
<li><a title="Ethics of artificial intelligence" href="https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence">Ethics</a></li>
<li><a title="Existential risk from artificial general intelligence" href="https://en.wikipedia.org/wiki/Existential_risk_from_artificial_general_intelligence">Existential risk</a></li>
<li><a title="Turing test" href="https://en.wikipedia.org/wiki/Turing_test">Turing test</a></li>
<li><a title="Chinese room" href="https://en.wikipedia.org/wiki/Chinese_room">Chinese room</a></li>
<li><a title="AI control problem" href="https://en.wikipedia.org/wiki/AI_control_problem">Control problem</a></li>
<li><a title="Friendly artificial intelligence" href="https://en.wikipedia.org/wiki/Friendly_artificial_intelligence">Friendly AI</a></li>
</ul>
</td>
</tr>
<tr>
<th><a title="History of artificial intelligence" href="https://en.wikipedia.org/wiki/History_of_artificial_intelligence">History</a></th>
</tr>
<tr>
<td class="plainlist">
<ul>
<li><a title="Timeline of artificial intelligence" href="https://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence">Timeline</a></li>
<li><a title="Progress in artificial intelligence" href="https://en.wikipedia.org/wiki/Progress_in_artificial_intelligence">Progress</a></li>
<li><a title="AI winter" href="https://en.wikipedia.org/wiki/AI_winter">AI winter</a></li>
</ul>
</td>
</tr>
<tr>
<th>Technology</th>
</tr>
<tr>
<td class="plainlist">
<ul>
<li><a title="Applications of artificial intelligence" href="https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence">Applications</a></li>
<li><a title="List of artificial intelligence projects" href="https://en.wikipedia.org/wiki/List_of_artificial_intelligence_projects">Projects</a></li>
<li><a title="List of programming languages for artificial intelligence" href="https://en.wikipedia.org/wiki/List_of_programming_languages_for_artificial_intelligence">Programming languages</a></li>
</ul>
</td>
</tr>
<tr>
<th>Glossary</th>
</tr>
<tr>
<td class="plainlist">
<ul>
<li><a title="Glossary of artificial intelligence" href="https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence">Glossary</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
</br>

<p>In&nbsp;<a title="Computer science" href="https://en.wikipedia.org/wiki/Computer_science">computer science</a>,&nbsp;<strong>artificial intelligence</strong>&nbsp;(<strong>AI</strong>), sometimes called&nbsp;<strong>machine intelligence</strong>, is&nbsp;<a title="Intelligence" href="https://en.wikipedia.org/wiki/Intelligence">intelligence</a>&nbsp;demonstrated by&nbsp;<a title="Machine" href="https://en.wikipedia.org/wiki/Machine">machines</a>, in contrast to the&nbsp;<strong>natural intelligence</strong>&nbsp;displayed by&nbsp;<a title="Human intelligence" href="https://en.wikipedia.org/wiki/Human_intelligence">humans</a>&nbsp;and&nbsp;<a title="Animal cognition" href="https://en.wikipedia.org/wiki/Animal_cognition">animals</a>. Leading AI textbooks define the field as the study of "<a title="Intelligent agent" href="https://en.wikipedia.org/wiki/Intelligent_agent">intelligent agents</a>": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.&nbsp;Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the&nbsp;<a class="mw-redirect" title="Human mind" href="https://en.wikipedia.org/wiki/Human_mind">human mind</a>, such as "learning" and "problem solving".</p>
<p>As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the&nbsp;<a title="AI effect" href="https://en.wikipedia.org/wiki/AI_effect">AI effect</a>.&nbsp;A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."&nbsp;For instance,&nbsp;<a title="Optical character recognition" href="https://en.wikipedia.org/wiki/Optical_character_recognition">optical character recognition</a>&nbsp;is frequently excluded from things considered to be AI,&nbsp;having become a routine technology.&nbsp;Modern machine capabilities generally classified as AI include successfully&nbsp;<a class="mw-redirect" title="Natural language understanding" href="https://en.wikipedia.org/wiki/Natural_language_understanding">understanding human speech</a>,&nbsp;competing at the highest level in&nbsp;<a class="mw-redirect" title="Strategic game" href="https://en.wikipedia.org/wiki/Strategic_game">strategic game</a>&nbsp;systems (such as&nbsp;<a title="Chess" href="https://en.wikipedia.org/wiki/Chess">chess</a>&nbsp;and&nbsp;<a title="Go (game)" href="https://en.wikipedia.org/wiki/Go_(game)">Go</a>),&nbsp;<a class="mw-redirect" title="Autonomous car" href="https://en.wikipedia.org/wiki/Autonomous_car">autonomously operating cars</a>, intelligent routing in&nbsp;<a title="Content delivery network" href="https://en.wikipedia.org/wiki/Content_delivery_network">content delivery networks</a>, and&nbsp;<a class="mw-redirect" title="Military simulations" href="https://en.wikipedia.org/wiki/Military_simulations">military simulations</a>.</p>
<p>Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism,&nbsp;followed by disappointment and the loss of funding (known as an "<a title="AI winter" href="https://en.wikipedia.org/wiki/AI_winter">AI winter</a>"),&nbsp;followed by new approaches, success and renewed funding.&nbsp;For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other.&nbsp;These sub-fields are based on technical considerations, such as particular goals (e.g. "<a title="Robotics" href="https://en.wikipedia.org/wiki/Robotics">robotics</a>" or "<a title="Machine learning" href="https://en.wikipedia.org/wiki/Machine_learning">machine learning</a>"),&nbsp;the use of particular tools ("<a title="Logic" href="https://en.wikipedia.org/wiki/Logic">logic</a>" or&nbsp;<a title="Artificial neural network" href="https://en.wikipedia.org/wiki/Artificial_neural_network">artificial neural networks</a>), or deep philosophical differences.&nbsp;Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).</p>
<p>The traditional problems (or goals) of AI research include&nbsp;<a title="Automated reasoning" href="https://en.wikipedia.org/wiki/Automated_reasoning">reasoning</a>,&nbsp;<a class="mw-redirect" title="Knowledge representation" href="https://en.wikipedia.org/wiki/Knowledge_representation">knowledge representation</a>,&nbsp;<a title="Automated planning and scheduling" href="https://en.wikipedia.org/wiki/Automated_planning_and_scheduling">planning</a>,&nbsp;<a title="Machine learning" href="https://en.wikipedia.org/wiki/Machine_learning">learning</a>,&nbsp;<a title="Natural language processing" href="https://en.wikipedia.org/wiki/Natural_language_processing">natural language processing</a>,&nbsp;<a title="Machine perception" href="https://en.wikipedia.org/wiki/Machine_perception">perception</a>&nbsp;and the ability to move and manipulate objects.&nbsp;<a title="Artificial general intelligence" href="https://en.wikipedia.org/wiki/Artificial_general_intelligence">General intelligence</a>&nbsp;is among the field's long-term goals.&nbsp;Approaches include&nbsp;<a href="https://en.wikipedia.org/wiki/Artificial_intelligence#Statistical">statistical methods</a>,&nbsp;<a href="https://en.wikipedia.org/wiki/Artificial_intelligence#Sub-symbolic">computational intelligence</a>, and&nbsp;<a href="https://en.wikipedia.org/wiki/Artificial_intelligence#Symbolic">traditional symbolic AI</a>. Many tools are used in AI, including versions of&nbsp;<a href="https://en.wikipedia.org/wiki/Artificial_intelligence#Search_and_optimization">search and mathematical optimization</a>,&nbsp;<a title="Artificial neural network" href="https://en.wikipedia.org/wiki/Artificial_neural_network">artificial neural networks</a>, and&nbsp;<a href="https://en.wikipedia.org/wiki/Artificial_intelligence#Probabilistic_methods_for_uncertain_reasoning">methods based on statistics, probability and economics</a>. The AI field draws upon&nbsp;<a title="Computer science" href="https://en.wikipedia.org/wiki/Computer_science">computer science</a>,&nbsp;<a title="Information engineering (field)" href="https://en.wikipedia.org/wiki/Information_engineering_(field)">information engineering</a>,&nbsp;<a title="Mathematics" href="https://en.wikipedia.org/wiki/Mathematics">mathematics</a>,&nbsp;<a title="Psychology" href="https://en.wikipedia.org/wiki/Psychology">psychology</a>,&nbsp;<a title="Linguistics" href="https://en.wikipedia.org/wiki/Linguistics">linguistics</a>,&nbsp;<a title="Philosophy" href="https://en.wikipedia.org/wiki/Philosophy">philosophy</a>, and many other fields.</p>
<p>The field was founded on the assumption that&nbsp;<a title="Human intelligence" href="https://en.wikipedia.org/wiki/Human_intelligence">human intelligence</a>&nbsp;"can be so precisely described that a machine can be made to simulate it".&nbsp;This raises philosophical arguments about the nature of the&nbsp;<a title="Mind" href="https://en.wikipedia.org/wiki/Mind">mind</a>&nbsp;and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by&nbsp;<a class="mw-redirect" title="History of AI" href="https://en.wikipedia.org/wiki/History_of_AI#AI_in_myth,_fiction_and_speculation">myth</a>,&nbsp;<a title="Artificial intelligence in fiction" href="https://en.wikipedia.org/wiki/Artificial_intelligence_in_fiction">fiction</a>&nbsp;and&nbsp;<a class="mw-redirect" title="Philosophy of AI" href="https://en.wikipedia.org/wiki/Philosophy_of_AI">philosophy</a>&nbsp;since&nbsp;<a title="Ancient history" href="https://en.wikipedia.org/wiki/Ancient_history">antiquity</a>.&nbsp;Some people also consider AI to be&nbsp;<a class="mw-redirect" title="Existential risk" href="https://en.wikipedia.org/wiki/Existential_risk">a danger to humanity</a>&nbsp;if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a&nbsp;<a title="Technological unemployment" href="https://en.wikipedia.org/wiki/Technological_unemployment#21st_century">risk of mass unemployment</a>.</p>
<p>In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in&nbsp;<a title="Computer performance" href="https://en.wikipedia.org/wiki/Computer_performance">computer power</a>, large amounts of&nbsp;<a title="Big data" href="https://en.wikipedia.org/wiki/Big_data">data</a>, and theoretical understanding; and AI techniques have become an essential part of the&nbsp;<a class="mw-redirect" title="Technology industry" href="https://en.wikipedia.org/wiki/Technology_industry">technology industry</a>, helping to solve many challenging problems in computer science,&nbsp;<a title="Software engineering" href="https://en.wikipedia.org/wiki/Software_engineering">software engineering</a>&nbsp;and&nbsp;<a title="Operations research" href="https://en.wikipedia.org/wiki/Operations_research">operations research</a>.</p>
</br>

<h2>Lectures </h2>

<ul>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(1).pdf" style="text-decoration:none;">Artificial Intelligence</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(2).pdf" style="text-decoration:none;">CSC384: Introduction to Artificial Intelligence  </a></li>
                                <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(3).pdf" style="text-decoration:none;">CptS 440 / 540 Artificial Intelligence: Introduction</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(4).pdf" style="text-decoration:none;">CS3243 Foundations Of Artificial Intelligence</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(5).pdf" style="text-decoration:none;">CMSC471/671 Artificial Intelligence</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(6).pdf" style="text-decoration:none;">Introduction to Neural Networks and Deep Learning</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(7).pdf" style="text-decoration:none;">Lecture Notes in Artificial Intelligence: Engineering Societies in the Agents World V</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(8).pdf" style="text-decoration:none;">CSE176 Introduction to Machine Learning — Lecture notes </a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(9).pdf" style="text-decoration:none;">Mathematics for Machine Learning</a></li>                             
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(10).pdf" style="text-decoration:none;">Lectures on Machine Learning: From artificial intelligence to machine learning </a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(11).pdf" style="text-decoration:none;">CS725: Foundations of Machine learning - Lecture Notes</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(12).pdf" style="text-decoration:none;">Lecture Notes On Artificial Intelligence</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(13).pdf" style="text-decoration:none;">Introduction to Artificial Intelligence</a></li>
                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(15).pdf" style="text-decoration:none;">Machine Learning and Data Mining Lecture Notes</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(16).pdf" style="text-decoration:none;">Artificial Intelligence - IIT</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(17).pdf" style="text-decoration:none;">Statistical Machine Learning </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(19).pdf" style="text-decoration:none;">Lecture Notes in Machine Learning </a></li>
 
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(20).pdf" style="text-decoration:none;">Introduction to AI and Intelligent Systems </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(21).pdf" style="text-decoration:none;">Machine Learning by Andrew NG Lecture Notes </a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(22).pdf" style="text-decoration:none;">Computing Machinery And Intelligence </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(23).pdf" style="text-decoration:none;">Concise Machine Learning </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(24).pdf" style="text-decoration:none;">Artificial Intelligence - MIT </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(14).pdf" style="text-decoration:none;">Artificial Intelligence Lecture Notes </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(18).pdf" style="text-decoration:none;">Lecture Notes in Artificial Intelligence: Contemporary Knowledge Engineering and Cognition </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(25).pdf" style="text-decoration:none;">Artificial Intelligence (Künstliche Intelligenz) Lecture Notes </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(26).pdf" style="text-decoration:none;">15-381: Artificial Intelligence - Introduction and Overview </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(27).pdf" style="text-decoration:none;">Python code for Artificial Intelligence: Foundations of Computational Agents </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(28).pdf" style="text-decoration:none;">Introduction to Computer Vision </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(30).rar" style="text-decoration:none;">Deep Learning </a></li> 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(29).pdf" style="text-decoration:none;">The Robot and I: How New Digital Technologies Are Making Smart People and Businesses Smarter by Automating Rote Work </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(31).pdf" style="text-decoration:none;">Artificial Intelligence And Life In 2030 </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(32).pdf" style="text-decoration:none;">Research Priorities for Robust and Beneficial Artificial Intelligence </a></li> 
 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(33).pdf" style="text-decoration:none;">Artificial Intelligence as a Positive and Negative Factor in Global Risk</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/ail(34).pdf" style="text-decoration:none;">Artificial Intelligence, Robotics, Privacy and Data Protection </a></li> 
     <li><a target="_blank" href="https://github.com/manjunath5496/Machine-Learning-Lectures/blob/master/README.md" style="text-decoration:none;">Machine Learning Lectures </a></li>
                          
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(1).pdf" style="text-decoration:none;">Artificial Intelligence and Human Thinking</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(2).pdf" style="text-decoration:none;">Artificial Intelligence and its Role in Near Future  </a></li>
                                <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(3).pdf" style="text-decoration:none;">Future Progress in Artificial Intelligence: A Survey of Expert Opinion</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(4).pdf" style="text-decoration:none;">AI | Professor John McCarthy</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(5).pdf" style="text-decoration:none;">Artificial Intelligence: Adversarial Search</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(6).pdf" style="text-decoration:none;">Artificial Intelligence: Constraint Satisfaction Problems</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(7).pdf" style="text-decoration:none;">Artificial Intelligence: Intelligent Agents</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(8).pdf" style="text-decoration:none;">Artificial Intelligence: Logical Agents</a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(9).pdf" style="text-decoration:none;">Artificial Intelligence: Machine Learning Association Rules</a></li>                             
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(10).pdf" style="text-decoration:none;">Classification </a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(11).pdf" style="text-decoration:none;">Artificial Intelligence: Machine Learning Ensemble Methods</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(12).pdf" style="text-decoration:none;">Machine Learning: Linear Models</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(13).pdf" style="text-decoration:none;">Artificial Intelligence: Machine Learning Logistic Regression</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(14).pdf" style="text-decoration:none;">Artificial Intelligence: Machine Learning Naive Bayes</a></li>                             
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(15).pdf" style="text-decoration:none;">Artificial Intelligence: Machine Learning Neural Networks</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(16).pdf" style="text-decoration:none;">Artificial Intelligence: Machine Learning Tree classifiers</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(17).pdf" style="text-decoration:none;">Artificial Intelligence: Machine Learning Unsupervised learning </a></li>                              
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(18).pdf" style="text-decoration:none;">Artificial Intelligence: Natural Language Processing </a></li>   
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(19).pdf" style="text-decoration:none;">Artificial Intelligence: Search Agents </a></li>
 
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(20).pdf" style="text-decoration:none;">Artificial Intelligence: Search Agents Uninformed search </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(21).pdf" style="text-decoration:none;">Artificial Intelligence: Search Agents Informed search </a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(22).pdf" style="text-decoration:none;">Artificial Intelligence: Ethics, governance and policy challenges </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(23).pdf" style="text-decoration:none;">Artificial Intelligence and Robotics and Their Impact on the Workplace </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(24).pdf" style="text-decoration:none;">Artificial Intelligence and the Future of Defense</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(25).pdf" style="text-decoration:none;">Artificial intelligence (AI) in healthcare and research</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(26).pdf" style="text-decoration:none;">Artificial intelligence in India – hype or reality Impact of artificial intelligence across industries and user groups</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(27).pdf" style="text-decoration:none;">Core Python: Creating Beautiful Code with an Interpreted, Dynamically Typed Language </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(28).pdf" style="text-decoration:none;">Data Mining and Statistics: What's the Connection?</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(29).pdf" style="text-decoration:none;">Essay: Artificial Intelligence: Boon or Bane?</a></li>
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(30).pdf" style="text-decoration:none;">Machine Learning that Matters</a></li> 
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(31).pdf" style="text-decoration:none;">Robot Path Planning </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(32).pdf" style="text-decoration:none;">Preparing for the Future of Artificial Intelligence </a></li> 
 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(33).pdf" style="text-decoration:none;">The Quest for Artificial Intelligence: A History of Ideas and Achievements</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(34).pdf" style="text-decoration:none;">Regulation of Artificial Intelligence in Selected Jurisdictions</a></li> 
 
 
                         
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(35).pdf" style="text-decoration:none;">Reinforcement Learning</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(36).pdf" style="text-decoration:none;">The Artificial Intelligence Black Box and the Failure of Intent and Causation </a></li>
                                <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(37).pdf" style="text-decoration:none;">The economics of artificial intelligence: Implications for the future of work</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(38).pdf" style="text-decoration:none;">Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(39).pdf" style="text-decoration:none;">Network Dissection: Quantifying Interpretability of Deep Visual Representations</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(40).pdf" style="text-decoration:none;">Interpretable Explanations of Black Boxes by Meaningful Perturbation</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(41).pdf" style="text-decoration:none;">A brief survey of visualization methods for deep learning models from the perspective of Explainable AI.</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(42).pdf" style="text-decoration:none;">Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks</a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(43).pdf" style="text-decoration:none;">Neural network inversion beyond gradient descent</a></li>                             
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(44).pdf" style="text-decoration:none;">NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(45).pdf" style="text-decoration:none;">Towards Robust Interpretability with Self-Explaining Neural Networks</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(46).pdf" style="text-decoration:none;">Axiomatic Attribution for Deep Networks</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(47).pdf" style="text-decoration:none;">Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(48).pdf" style="text-decoration:none;">Explainable Artificial Intelligence via Bayesian Teaching</a></li>                             
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(49).pdf" style="text-decoration:none;">Understanding Neural Networks Through Deep Visualization</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(50).pdf" style="text-decoration:none;">Visualizing and Understanding Convolutional Networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(51).pdf" style="text-decoration:none;">Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps </a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(52).pdf" style="text-decoration:none;">Striving for Simplicity: The All Convolutional Net</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(53).pdf" style="text-decoration:none;">"Why Should I Trust You?": Explaining the Predictions of Any Classifier </a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(54).pdf" style="text-decoration:none;">Not Just A Black Box: Learning Important Features Through Propagating Activation Differences </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(55).pdf" style="text-decoration:none;">A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(56).pdf" style="text-decoration:none;">Visualizing Deep Neural Network Decisions: Prediction Difference Analysis</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(57).pdf" style="text-decoration:none;">Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(58).pdf" style="text-decoration:none;">Towards A Rigorous Science of Interpretable Machine Learning</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(59).pdf" style="text-decoration:none;">Methods for Interpreting and Understanding Deep Neural Networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(60).pdf" style="text-decoration:none;">Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(61).pdf" style="text-decoration:none;">Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks</a></li>
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(62).pdf" style="text-decoration:none;">Understanding Deep Architectures by Visual Summaries</a></li> 
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(63).pdf" style="text-decoration:none;">A Survey Of Methods: For Explaining Black Box Models</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(64).pdf" style="text-decoration:none;">Neural Network Interpretation via Fine-Grained Textual Summarization </a></li> 
 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(65).pdf" style="text-decoration:none;">How Important Is a Neuron?</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(66).pdf" style="text-decoration:none;">Explaining Explanations: An Overview of Interpretability of Machine Learning</a></li> 
 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(67).pdf" style="text-decoration:none;">RISE: Randomized Input Sampling for Explanation of Black-box Models</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(68).pdf" style="text-decoration:none;">Explaining Image Classifiers by Counterfactual Generation</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(69).pdf" style="text-decoration:none;">Diverse feature visualizations reveal invariances in early layers of deep neural networks</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(70).pdf" style="text-decoration:none;">Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(71).pdf" style="text-decoration:none;">Improving the Interpretability of Deep Neural Networks with Knowledge Distillation</a></li>                             
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(72).pdf" style="text-decoration:none;">Interpretable machine learning: definitions, methods, and applications</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(73).pdf" style="text-decoration:none;">On the (In)fidelity and Sensitivity of Explanations</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(74).pdf" style="text-decoration:none;">Interpreting Black Box Models via Hypothesis Testing </a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(75).pdf" style="text-decoration:none;">Counterfactual Visual Explanations</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(76).pdf" style="text-decoration:none;">Understanding Neural Networks via Feature Visualization: A survey </a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(77).pdf" style="text-decoration:none;">Visualizing Deep Networks by Optimizing with Integrated Gradients </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(78).pdf" style="text-decoration:none;">Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(79).pdf" style="text-decoration:none;">A Unified Approach to Interpreting Model Predictions</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(80).pdf" style="text-decoration:none;">Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(81).pdf" style="text-decoration:none;">Sanity Checks for Saliency Maps</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(82).pdf" style="text-decoration:none;">PythonRobotics: a Python code collection of robotics algorithms</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(83).pdf" style="text-decoration:none;">A.I. as an Introduction to Research Methods in Computer Science</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(84).pdf" style="text-decoration:none;">Leibniz: Explanation of Binary Arithmetic (1703)</a></li>
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(85).pdf" style="text-decoration:none;">The Mathematical Analysis of Logic</a></li> 
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(86).pdf" style="text-decoration:none;">On formally undecidable propositions of Principia Mathematica and related systems I</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(87).pdf" style="text-decoration:none;">On Computable Numbers, with an Application to the Entscheidungsproblem </a></li> 
 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(88).pdf" style="text-decoration:none;">A Logical Calculus of the Ideas Immanent in Nervous Activity</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(89).pdf" style="text-decoration:none;">The First Draft of a Report on the EDVAC</a></li> 
 
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(90).pdf" style="text-decoration:none;">Computing Machinery and Intelligence</a></li> 
 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(91).pdf" style="text-decoration:none;"> Statistical and inductive probability</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(92).pdf" style="text-decoration:none;">Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(93).pdf" style="text-decoration:none;">Steps Toward Artificial Intelligence</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(94).pdf" style="text-decoration:none;">The Working Set Model for Program</a></li>
               <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(95).pdf" style="text-decoration:none;">Human problem solving: The state of the theory in 1970</a></li>                             
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(96).pdf" style="text-decoration:none;"> Computer Science as Empirical Inquiry: Symbols and Search</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(97).pdf" style="text-decoration:none;">The Knowledge Level</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(98).pdf" style="text-decoration:none;">Parallel Distributed Processing, Explorations in the microstructure of Cognition, Volume 1: Foundations</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(99).pdf" style="text-decoration:none;">Intelligence without representation</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(100).pdf" style="text-decoration:none;">Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(101).pdf" style="text-decoration:none;">Gradient-Based Learning Applied to Document Recognition </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(102).pdf" style="text-decoration:none;">The PageRank Citation Ranking: Bringing Order to the Web </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(103).pdf" style="text-decoration:none;">A Neural Probabilistic Language Model</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(104).pdf" style="text-decoration:none;">Deep Learning of Representations: Looking Forward</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(105).pdf" style="text-decoration:none;">Learning Deep Architectures for AI</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(106).pdf" style="text-decoration:none;">Wikipedia-based Semantic Interpretation for Natural Language Processing</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(107).pdf" style="text-decoration:none;">YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(108).pdf" style="text-decoration:none;">Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization</a></li>
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(109).pdf" style="text-decoration:none;">Playing Atari with Deep Reinforcement Learning</a></li> 
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(110).pdf" style="text-decoration:none;">DeepFace: Closing the Gap to Human-Level Performance in Face Verification</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(111).pdf" style="text-decoration:none;">Human-level control through deep reinforcement learning </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(112).pdf" style="text-decoration:none;">A (Very) Brief History of Artificial Intelligence</a></li> 
 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(113).pdf" style="text-decoration:none;">History Of Artificial Intelligence (AI)</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(114).pdf" style="text-decoration:none;">Machine Learning In Self-driving Tesla Automobiles </a></li> 
   
 
  <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/air(115).pdf" style="text-decoration:none;">Human Side of Tesla Autopilot: Exploration of Functional Vigilance in Real-World Human-Machine Collaboration </a></li> 
 
 
 
 
 
 
 
  </ul>

</br>
  <h2>The 18 Best Books About AI: </h2>
  
  <ul>
 <li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(1).pdf" style="text-decoration:none;">Affective Computing: Focus on Emotion Expression, Synthesis and Recognition</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(2).pdf" style="text-decoration:none;">Common LISP: A Gentle Introduction to Symbolic Computation</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(3).pdf" style="text-decoration:none;">Planning Algorithms</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(4).pdf" style="text-decoration:none;"> Artificial Intelligence: Foundations of Computational Agents</a></li>
                               
  <li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(5).pdf" style="text-decoration:none;"> A Course in Machine Learning </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(6).pdf" style="text-decoration:none;">Clever Algorithms: Nature-Inspired Programming Recipes</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(7).pdf" style="text-decoration:none;">Deep Learning with R</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(8).pdf" style="text-decoration:none;">Essentials of Metaheuristics</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(9).pdf" style="text-decoration:none;"> From Bricks to Brains: The Embodied Cognitive Science of LEGO Robots </a></li>
                               
<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(10).pdf" style="text-decoration:none;">Logic For Computer Science: Foundations of Automatic Theorem Proving </a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(11).pdf" style="text-decoration:none;">Life 3.0: Being Human in the Age of Artificial Intelligence </a></li>
                               
  <li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(12).pdf" style="text-decoration:none;"> Our Final Invention: Artificial Intelligence and the End of the Human Era</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(13).rar" style="text-decoration:none;">Artificial Intelligence: A Modern Approach </a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(14).rar" style="text-decoration:none;">Python Machine Learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(15).pdf" style="text-decoration:none;">The Quest for Artificial Intelligence: A History of Ideas and Achievements</a></li>
  
<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(16).pdf" style="text-decoration:none;"> Simply Logical: Intelligent Reasoning by Example</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(17).pdf" style="text-decoration:none;"> Superintelligence</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/The-18-Best-Books-About-AI/blob/master/ai(18).pdf" style="text-decoration:none;">Virtual Reality for Human Computer Interaction</a></li>

</ul>
</br>
 <h2>Physics ML Papers: </h2>
 
 <ul>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(1).pdf" style="text-decoration:none;">Quantum algorithms for supervised and unsupervised machine learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(2).pdf" style="text-decoration:none;">Understanding Machine-learned Density Functionals</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(3).pdf" style="text-decoration:none;">Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(4).pdf" style="text-decoration:none;">An exact mapping between the Variational Renormalization Group and Deep Learning</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(5).pdf" style="text-decoration:none;">Automated Search for new Quantum Experiments</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(6).pdf" style="text-decoration:none;">Quantum gate learning in qubit networks: Toffoli gate without time dependent control</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(7).pdf" style="text-decoration:none;">Quantum Boltzmann Machine</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(8).pdf" style="text-decoration:none;"> Machine learning phases of matter </a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(9).pdf" style="text-decoration:none;">Exponential Machines</a></li>
  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(10).pdf" style="text-decoration:none;">Supervised Learning With Quantum-Inspired Tensor Networks </a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(11).pdf" style="text-decoration:none;">Discovering Phase Transitions with Unsupervised Learning</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(12).pdf" style="text-decoration:none;">Solving the Quantum Many-Body Problem with Artificial Neural Networks</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(13).pdf" style="text-decoration:none;">Learning Thermodynamics with Boltzmann Machines</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(14).pdf" style="text-decoration:none;">Machine learning quantum phases of matter beyond the fermion sign problem</a></li>
                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(15).pdf" style="text-decoration:none;">Why does deep and cheap learning work so well?</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(16).pdf" style="text-decoration:none;">Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(17).pdf" style="text-decoration:none;">Machine Learning Phases of Strongly Correlated Fermions</a></li>   
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(18).pdf" style="text-decoration:none;">By-passing the Kohn-Sham equations with machine learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(19).pdf" style="text-decoration:none;">Pure density functional for strong correlations and the thermodynamic limit from machine learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(20).pdf" style="text-decoration:none;">Machine learning topological states</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(21).pdf" style="text-decoration:none;">Learning phase transitions by confusion</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(22).pdf" style="text-decoration:none;">Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines</a></li> 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(23).pdf" style="text-decoration:none;">Self-Learning Monte Carlo Method</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(24).pdf" style="text-decoration:none;">A Neural Decoder for Topological Codes</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(25).pdf" style="text-decoration:none;">Quantum Loop Topography for Machine Learning</a></li>                              
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(26).pdf" style="text-decoration:none;">Sampling algorithms for validation of supervised learning models for Ising-like systems</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(27).pdf" style="text-decoration:none;">Quantum Machine Learning</a></li>
   
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(28).pdf" style="text-decoration:none;">Self-Learning Monte Carlo Method in Fermion Systems</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(29).pdf" style="text-decoration:none;">Tomography and Generative Data Modeling via Quantum Boltzmann Training</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(30).pdf" style="text-decoration:none;">Reinforcement Learning Using Quantum Boltzmann Machines</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(31).pdf" style="text-decoration:none;">Restricted Boltzmann Machines for the Long Range Ising Models</a></li> 
    <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(32).pdf" style="text-decoration:none;">Equivalence of restricted Boltzmann machines and tensor network states</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(33).pdf" style="text-decoration:none;">Quantum Entanglement in Neural Network States</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(34).pdf" style="text-decoration:none;">Efficient Representation of Quantum Many-body States with Deep Neural Networks</a></li> 
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(35).pdf" style="text-decoration:none;">Neural network representation of tensor network and chiral states</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(36).pdf" style="text-decoration:none;">Opening the black box of Deep Neural Networks via Information</a></li> 
 
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(37).pdf" style="text-decoration:none;">Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(38).pdf" style="text-decoration:none;">Neural-network quantum state tomography for many-body systems</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(39).pdf" style="text-decoration:none;">Experimental Quantum Hamiltonian Learning</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(40).pdf" style="text-decoration:none;">Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(41).pdf" style="text-decoration:none;">Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(42).pdf" style="text-decoration:none;">Probing many-body localization with neural networks</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(43).pdf" style="text-decoration:none;">Approximating quantum many-body wave-functions using artificial neural networks</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(44).pdf" style="text-decoration:none;">Deterministic Quantum Annealing Expectation-Maximization Algorithm</a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(45).pdf" style="text-decoration:none;">Kernel methods for interpretable machine learning of order parameters</a></li>  
   
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(46).pdf" style="text-decoration:none;">Mutual Information, Neural Networks and the Renormalization Group</a></li> 
                             
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(47).pdf" style="text-decoration:none;">Nonequilibrium Thermodynamics of Restricted Boltzmann Machines</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(48).pdf" style="text-decoration:none;">Reinforcement Learning in Different Phases of Quantum Control</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(49).pdf" style="text-decoration:none;">Decoding Small Surface Codes with Feedforward Neural Networks </a></li>
                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(50).pdf" style="text-decoration:none;">A Separability-Entanglement Classifier via Machine Learning</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(51).pdf" style="text-decoration:none;">Machine Learning Z<sub>2</sub> Quantum Spin Liquids with Quasi-particle Statistics</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(52).pdf" style="text-decoration:none;">Construction of Hamiltonians by supervised learning of energy and entanglement spectra</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(53).pdf" style="text-decoration:none;">Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory </a></li>
 
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(54).pdf" style="text-decoration:none;">Self-Learning Monte Carlo Method: Continuous-Time Algorithm </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(55).pdf" style="text-decoration:none;">Machine-learning-assisted correction of correlated qubit errors in a topological code</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(56).pdf" style="text-decoration:none;">Criticality and Deep Learning II: Momentum Renormalisation Group </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(57).pdf" style="text-decoration:none;">Active learning machine learns to create new quantum experiments </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(58).pdf" style="text-decoration:none;">Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis</a></li>
    <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(59).pdf" style="text-decoration:none;">Self-Learning Phase Boundaries by Active Contours</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(60).pdf" style="text-decoration:none;">Inverse Ising inference by combining Ornstein-Zernike theory with deep learning </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(61).pdf" style="text-decoration:none;">Deep neural networks for direct, featureless learning through observation: the case of 2d spin models</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(62).pdf" style="text-decoration:none;">Quantum phase recognition via unsupervised machine learning</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(63).pdf" style="text-decoration:none;">Learning the Einstein-Podolsky-Rosen correlations on a Restricted Boltzmann Machine</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(64).pdf" style="text-decoration:none;">Quantum dynamics in transverse-field Ising models from classical networks</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(65).pdf" style="text-decoration:none;">Quantum machine learning: a classical perspective </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(66).pdf" style="text-decoration:none;">Solving the Bose-Hubbard model with machine learning</a></li> 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(67).pdf" style="text-decoration:none;">Spectral Dynamics of Learning Restricted Boltzmann Machines</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(68).pdf" style="text-decoration:none;">Deep Learning the Ising Model Near Criticality</a></li> 
 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(69).pdf" style="text-decoration:none;">Learning Fermionic Critical Points</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(70).pdf" style="text-decoration:none;">Extensive deep neural networks</a></li> 
  
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(71).pdf" style="text-decoration:none;">Machine Learning Topological Invariants with Neural Networks</a></li>
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(72).pdf" style="text-decoration:none;">Phase Diagrams of Three-Dimensional Anderson and Quantum Percolation Models using Deep Three-Dimensional Convolutional
Neural Network</a></li> 
 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(73).pdf" style="text-decoration:none;">Machine Learning Spatial Geometry from Entanglement Features</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(74).pdf" style="text-decoration:none;">Unsupervised Generative Modeling Using Matrix Product States</a></li>
    <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(75).pdf" style="text-decoration:none;">Identifying Product Order with Restricted Boltzmann Machines</a></li>                        
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(76).pdf" style="text-decoration:none;">Machine learning and artificial intelligence in the quantum domain</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(77).pdf" style="text-decoration:none;">Learning Disordered Topological Phases by Statistical Recovery of Symmetry</a></li> 
 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(78).pdf" style="text-decoration:none;">Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(79).pdf" style="text-decoration:none;">Quantum Autoencoders via Quantum Adders with Genetic Algorithms</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(80).pdf" style="text-decoration:none;">Generalized Quantum Reinforcement Learning with Quantum Technologies</a></li> 
 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(81).pdf" style="text-decoration:none;">Combining Machine Learning and Physics to Understand Glassy Systems</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(82).pdf" style="text-decoration:none;">Enhanced Quantum Synchronization via Quantum Machine Learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(83).pdf" style="text-decoration:none;">Learning hard quantum distributions with variational autoencoders</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(84).pdf" style="text-decoration:none;">Mean-field theory of input dimensionality reduction in unsupervised deep neural networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(85).pdf" style="text-decoration:none;">Neural Networks Quantum States, String-Bond States and chiral topological states</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(86).pdf" style="text-decoration:none;">Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(87).pdf" style="text-decoration:none;">Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(88).pdf" style="text-decoration:none;">A Correspondence Between Random Neural Networks and Statistical Field Theory</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(89).pdf" style="text-decoration:none;">Learning Hidden Quantum Markov Models</a></li>
  
  
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(90).pdf" style="text-decoration:none;">Machine learning vortices at the Kosterlitz-Thouless transition</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(91).pdf" style="text-decoration:none;">Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(92).pdf" style="text-decoration:none;">Machine learning out-of-equilibrium phases of matter</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(93).pdf" style="text-decoration:none;"> An efficient quantum algorithm for generative machine learning</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(94).pdf" style="text-decoration:none;">Hardening Quantum Machine Learning Against Adversaries</a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(95).pdf" style="text-decoration:none;">Experimental learning of quantum states</a></li>  
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(96).pdf" style="text-decoration:none;">Deep Neural Network Detects Quantum Phase Transition</a></li> 
  
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(97).pdf" style="text-decoration:none;">Towards reduction of autocorrelation in HMC by machine learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(98).pdf" style="text-decoration:none;">Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines</a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(99).pdf" style="text-decoration:none;">A Quantum Extension of Variational Bayes Inference</a></li>  
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(100).pdf" style="text-decoration:none;">A quantum algorithm to train neural networks using low-depth circuits</a></li>  
  
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(101).pdf" style="text-decoration:none;">Pattern recognition techniques for Boson Sampling validation</a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(102).pdf" style="text-decoration:none;">Learning Relevant Features of Data with Multi-scale Tensor Networks</a></li> 
  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(103).pdf" style="text-decoration:none;">A relativistic extension of Hopfield neural networks via the mechanical analogy </a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(104).pdf" style="text-decoration:none;">Generative Models for Stochastic Processes Using Convolutional Neural Networks</a></li>  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(105).pdf" style="text-decoration:none;">Scale-invariant Feature Extraction of Neural Network and Renormalization Group Flow</a></li> 
 
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(106).pdf" style="text-decoration:none;">Experimentally Detecting a Quantum Change Point via Bayesian Inference</a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(107).pdf" style="text-decoration:none;">Leveraging Adiabatic Quantum Computation for Election Forecasting</a></li> 
  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(108).pdf" style="text-decoration:none;">Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification</a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(109).pdf" style="text-decoration:none;">Critical Percolation as a Framework to Analyze the Training of Deep Networks</a></li>  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(110).pdf" style="text-decoration:none;">Neural Network Renormalization Group</a></li>  
   
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(111).pdf" style="text-decoration:none;">Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control</a></li> 
  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(112).pdf" style="text-decoration:none;">Reinforcement Learning with Neural Networks for Quantum Feedback</a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(113).pdf" style="text-decoration:none;">Learning DNFs under product distributions via μ–biased quantum Fourier sampling</a></li>  
   
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(114).pdf" style="text-decoration:none;">Inferring relevant features: from QFT to PCA</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(115).pdf" style="text-decoration:none;">Quantum Variational Autoencoder</a></li>  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(116).pdf" style="text-decoration:none;">The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity</a></li>   
   
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(117).pdf" style="text-decoration:none;">Deep neural decoders for near term fault-tolerant experiments</a></li>  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(118).pdf" style="text-decoration:none;">Advantages of versatile neural-network decoding for topological codes</a></li>  
   
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(119).pdf" style="text-decoration:none;">Online Learning of Quantum States</a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(120).pdf" style="text-decoration:none;">Parameter diagnostics of phases and phase transition learning by neural networks</a></li>  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(121).pdf" style="text-decoration:none;">Universal Quantum Control through Deep Reinforcement Learning</a></li>   
   
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(122).pdf" style="text-decoration:none;">Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning </a></li>  
     
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(123).pdf" style="text-decoration:none;">Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics</a></li>  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(124).pdf" style="text-decoration:none;">Measurement-based adaptation protocol with quantum reinforcement learning</a></li>   
   
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(125).pdf" style="text-decoration:none;">Vulnerability of Deep Learning </a></li>   
   
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(126).pdf" style="text-decoration:none;">Comparing Dynamics: Deep Neural Networks versus Glassy Systems</a></li> 
   
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(127).pdf" style="text-decoration:none;">Extrapolating quantum observables with machine learning: Inferring multiple phase transitions from properties of a single phase</a></li>  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(128).pdf" style="text-decoration:none;">A high-bias, low-variance introduction to Machine Learning for physicists</a></li>   
   
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(129).pdf" style="text-decoration:none;">Deep Learning Phase Segregation</a></li>   
   
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(130).pdf" style="text-decoration:none;">Learning architectures based on quantum entanglement: a simple matrix product state algorithm for image recognition </a></li>    
   
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(131).pdf" style="text-decoration:none;">Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks </a></li>   
   
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(132).pdf" style="text-decoration:none;">Quantum algorithms for training Gaussian Processes </a></li>   
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(133).pdf" style="text-decoration:none;">Matrix Product Operators for Sequence to Sequence Learning </a></li>     
   
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(134).pdf" style="text-decoration:none;">Protection against Cloning for Deep Learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(135).pdf" style="text-decoration:none;">Barren plateaus in quantum neural network training landscapes</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(136).pdf" style="text-decoration:none;">Learning quantum models from quantum or classical data</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(137).pdf" style="text-decoration:none;">Towards Quantum Machine Learning with Tensor Networks</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(138).pdf" style="text-decoration:none;">A note on state preparation for quantum machine learning</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(139).pdf" style="text-decoration:none;">Circuit-centric quantum classifiers</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(140).pdf" style="text-decoration:none;">Classical Verification of Quantum Computations</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(141).pdf" style="text-decoration:none;"> Smallest Neural Network to Learn the Ising Criticality</a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(142).pdf" style="text-decoration:none;">Quantum Machine Learning Matrix Product States</a></li>                             
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(143).pdf" style="text-decoration:none;">The Loss Surface of XOR Artificial Neural Networks</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(144).pdf" style="text-decoration:none;">Complex energy landscapes in spiked-tensor and simple glassy models: ruggedness, arrangements of local minima and phase transitions</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(145).pdf" style="text-decoration:none;">Machine learning of phase transitions in the percolation and XY models</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(146).pdf" style="text-decoration:none;">Neural network decoder for topological color codes with circuit level noise</a></li>
                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(147).pdf" style="text-decoration:none;">Variational quantum simulation of imaginary time evolution with applications in chemistry and beyond</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(148).pdf" style="text-decoration:none;">Strawberry Fields: A Software Platform for Photonic Quantum Computing</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(149).pdf" style="text-decoration:none;">Supervised machine learning algorithms based on generalized Gibbs ensembles</a></li>   
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(150).pdf" style="text-decoration:none;">Hierarchical quantum classifiers</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(151).pdf" style="text-decoration:none;">Differentiable Learning of Quantum Circuit Born Machine </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(152).pdf" style="text-decoration:none;">Optimizing a Polynomial Function on a Quantum Simulator</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(153).pdf" style="text-decoration:none;">Classicalization Clearly: Quantum Transition into States of Maximal Memory Storage Capacity</a></li> 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(154).pdf" style="text-decoration:none;">Method to solve quantum few-body problems with artificial neural networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(155).pdf" style="text-decoration:none;">A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(156).pdf" style="text-decoration:none;">Learning non-Higgsable gauge groups in 4D F-theory</a></li>                              
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(157).pdf" style="text-decoration:none;">A Simple Quantum Neural Net with a Periodic Activation Function</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(158).pdf" style="text-decoration:none;">Quantum codes from classical graphical models</a></li>
   
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(159).pdf" style="text-decoration:none;">Machine learning assisted readout of trapped-ion qubits </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(160).pdf" style="text-decoration:none;">Probing Hidden Spin Order with Interpretable Machine Learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(161).pdf" style="text-decoration:none;">Quantum generative adversarial networks</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(162).pdf" style="text-decoration:none;">Quantum generative adversarial learning</a></li> 
    <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(163).pdf" style="text-decoration:none;">Supervised learning with quantum enhanced feature spaces</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(164).pdf" style="text-decoration:none;">Multiparameter optimisation of a magneto-optical trap using deep learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(165).pdf" style="text-decoration:none;">Neural networks as Interacting Particle Systems: Asymptotic convexity of the Loss Landscape and Universal Scaling of the Approximation Error</a></li> 
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(166).pdf" style="text-decoration:none;">Optimal universal learning machines for quantum state discrimination</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(167).pdf" style="text-decoration:none;">Quantum artificial intelligence to simulate many body quantum systems</a></li> 
 
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(168).pdf" style="text-decoration:none;">Identifying topological order via unsupervised machine learning</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(169).pdf" style="text-decoration:none;">Physically optimizing inference</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(170).pdf" style="text-decoration:none;">Universal discriminative quantum neural networks</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(171).pdf" style="text-decoration:none;">Quantum classification of the MNIST dataset via Slow Feature Analysis</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(172).pdf" style="text-decoration:none;">Entropy and mutual information in models of deep neural networks</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(173).pdf" style="text-decoration:none;">Deep Learning Topological Invariants of Band Insulators</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(174).pdf" style="text-decoration:none;">Bayesian Quantum Circuit</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(175).pdf" style="text-decoration:none;">Machine learning many-body localization: Search for the elusive nonergodic metal</a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(176).pdf" style="text-decoration:none;">Automated discovery of characteristic features of phase transitions in many-body localization</a></li>  
   
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(177).pdf" style="text-decoration:none;">Adversarial quantum circuit learning for pure state approximation</a></li> 
                             
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(178).pdf" style="text-decoration:none;">Machine learning of quantum phase transitions</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(179).pdf" style="text-decoration:none;">Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(180).pdf" style="text-decoration:none;">Machine-learning Skyrmions</a></li>
                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(181).pdf" style="text-decoration:none;">Analytic continuation via 'domain-knowledge free' machine learning</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(182).pdf" style="text-decoration:none;">Supervised learning with generalized tensor networks </a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(183).pdf" style="text-decoration:none;">Continuous-variable quantum neural networks </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(184).pdf" style="text-decoration:none;">Decoherence in a quantum neural network </a></li>
 
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(185).pdf" style="text-decoration:none;">Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(186).pdf" style="text-decoration:none;">Quantum Codes from Neural Networks</a></li>
                             
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(187).pdf" style="text-decoration:none;">Artificial Quantum Neural Network: quantum neurons, logical elements and tests of convolutional nets</a></li>
                             
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(188).pdf" style="text-decoration:none;">A Universal Training Algorithm for Quantum Deep Learning </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(189).pdf" style="text-decoration:none;">Optimization of neural networks via finite-value quantum fluctuations</a></li>
    <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(190).pdf" style="text-decoration:none;">Adversarial training of quantum Born machine </a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(191).pdf" style="text-decoration:none;">Interpretable Machine Learning for Inferring the Phase Boundaries in a Non-equilibrium System  </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(192).pdf" style="text-decoration:none;">Benchmarking Neural Networks For Quantum Computations</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(193).pdf" style="text-decoration:none;">Symmetries and many-body excited states with neural-network quantum states</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(194).pdf" style="text-decoration:none;">Neural networks as "hidden" variable models for quantum systems </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(195).pdf" style="text-decoration:none;">Recurrent neural networks running on quantum spins: memory accuracy and capacity</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(196).pdf" style="text-decoration:none;">TherML: Thermodynamics of Machine Learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(197).pdf" style="text-decoration:none;">The fundamentals of quantum machine learning</a></li> 
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(198).pdf" style="text-decoration:none;">An introductory example of machine learning enhanced global optimization</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(199).pdf" style="text-decoration:none;">Self-learning Monte Carlo method with Behler-Parrinello neural networks</a></li> 
 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(200).pdf" style="text-decoration:none;">Many-body (de)localization in large quantum chains</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(201).pdf" style="text-decoration:none;">irbasis: Open-source database and software for intermediate-representation basis functions of imaginary-time Green's function </a></li> 
  
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(202).pdf" style="text-decoration:none;">Solving Many-Electron Schrodinger Equation Using Deep Neural Networks</a></li>
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(203).pdf" style="text-decoration:none;">Local-measurement-based quantum state tomography via neural networks</a></li> 
 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(204).pdf" style="text-decoration:none;">Machine learning study of the relationship between the geometric and entropy discord</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(205).pdf" style="text-decoration:none;">Solving frustrated quantum many-particle models with convolutional neural networks</a></li>
    <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(206).pdf" style="text-decoration:none;">Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers</a></li>                        
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(207).pdf" style="text-decoration:none;">Discovering physical concepts with neural networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(208).pdf" style="text-decoration:none;">Experimental Implementation of a Quantum Autoencoder via Quantum Adders</a></li> 
 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(209).pdf" style="text-decoration:none;">Quantized Hodgkin-Huxley Model for Quantum Neurons</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(210).pdf" style="text-decoration:none;">Backflow Transformations via Neural Networks for Quantum Many-Body Wave-Functions</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(211).pdf" style="text-decoration:none;">Machine learning method for state preparation and gate synthesis on photonic quantum computers</a></li> 
 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(212).pdf" style="text-decoration:none;">Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(213).pdf" style="text-decoration:none;">Geometry of energy landscapes and the optimizability of deep neural networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(214).pdf" style="text-decoration:none;">From Bloch Oscillations to Many Body Localization in Clean Interacting Systems</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(215).pdf" style="text-decoration:none;">Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(216).pdf" style="text-decoration:none;">Machine Learning Phase Transition: An Iterative Methodology</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(217).pdf" style="text-decoration:none;">Generalized Transfer Matrix States from Artificial Neural Networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(218).pdf" style="text-decoration:none;">Quantum Lyapunov control with machine learning</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(219).pdf" style="text-decoration:none;">Quantum generative adversarial learning in a superconducting quantum circuit</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(220).pdf" style="text-decoration:none;">Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks</a></li>
  
  
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(221).pdf" style="text-decoration:none;">Learning and Inference on Generative Adversarial Quantum Circuits</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(222).pdf" style="text-decoration:none;">Model Reduction with Memory and the Machine Learning of Dynamical Systems</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(223).pdf" style="text-decoration:none;">A machine-learning solver for modified diffusion equations</a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(224).pdf" style="text-decoration:none;"> Geometry and symmetry in quantum Boltzmann machine </a></li>
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(225).pdf" style="text-decoration:none;">CosmoFlow: Using Deep Learning to Learn the Universe at Scale </a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(226).pdf" style="text-decoration:none;">Neural-network states for the classical simulation of quantum computing</a></li>  
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(227).pdf" style="text-decoration:none;">Machine Learning Configuration Interaction </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(228).pdf" style="text-decoration:none;">Artificial Neural Networks in Fluid Dynamics: A Novel Approach to the Navier-Stokes Equations</a></li> 
  
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(229).pdf" style="text-decoration:none;">Smart energy models for atomistic simulations using a DFT-driven multifidelity approach</a></li>  
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(230).pdf" style="text-decoration:none;">Machine learning non-local correlations</a></li>  
   
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(231).pdf" style="text-decoration:none;">
Statistical Neurodynamics of Deep Networks: Geometry of Signal Spaces </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(232).pdf" style="text-decoration:none;">The effect of retardation in the random networks of excitable nodes embeddable in the Euclidean space</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(233).pdf" style="text-decoration:none;">Realizing quantum linear regression with auxiliary qumodes</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(234).pdf" style="text-decoration:none;">Quantum enhanced cross-validation for near-optimal neural networks architecture selection</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(235).pdf" style="text-decoration:none;">Policy Guided Monte Carlo: Reinforcement Learning Markov Chain Dynamics</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(236).pdf" style="text-decoration:none;">Reconstruction of a Photonic Qubit State with Quantum Reinforcement Learning</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(237).pdf" style="text-decoration:none;">Quantum optical neural networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(238).pdf" style="text-decoration:none;"> Deep learning, quantum chaos, and pseudorandom evolution </a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(239).pdf" style="text-decoration:none;">A Quantum Model for Multilayer Perceptron</a></li>                             
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(240).pdf" style="text-decoration:none;">Quantum Neural Network States</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(241).pdf" style="text-decoration:none;">Simple coarse graining and sampling strategies for image recognition</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(242).pdf" style="text-decoration:none;">GANs for generating EFT models</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(243).pdf" style="text-decoration:none;">Approaching the adiabatic timescale with machine-learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(244).pdf" style="text-decoration:none;">Projective quantum Monte Carlo simulations guided by unrestricted neural network states</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(245).pdf" style="text-decoration:none;">Supervised machine learning of ultracold atoms with speckle disorder</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(246).pdf" style="text-decoration:none;">Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(247).pdf" style="text-decoration:none;">Implementable Quantum Classifier for Nonlinear Data</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(248).pdf" style="text-decoration:none;">Neural Network Decoders for Large-Distance 2D Toric Codes</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(249).pdf" style="text-decoration:none;">The jamming transition as a paradigm to understand the loss landscape of deep neural networks</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(250).pdf" style="text-decoration:none;">Phase Diagram of Disordered Higher Order Topological Insulator: a Machine Learning Study</a></li> 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(251).pdf" style="text-decoration:none;">A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(252).pdf" style="text-decoration:none;">Monge-Ampère Flow for Generative Modeling</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(253).pdf" style="text-decoration:none;">Self-organizing maps as a method for detecting phase transitions and phase identification</a></li>                              
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(254).pdf" style="text-decoration:none;">Solving Statistical Mechanics using Variational Autoregressive Networks</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(255).pdf" style="text-decoration:none;">Deep learning systems as complex networks</a></li>
   
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(256).pdf" style="text-decoration:none;">Extracting many-particle entanglement entropy from observables using supervised machine learning </a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(257).pdf" style="text-decoration:none;">An Artificial Neural Network Approach to the Analytic Continuation Problem </a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(258).pdf" style="text-decoration:none;">A quantum autoencoder: the compression of qutrits via machine learning</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(259).pdf" style="text-decoration:none;">Efficient Representation of Topologically Ordered States with Restricted Boltzmann Machines</a></li> 
    <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(260).pdf" style="text-decoration:none;">Super-resolving the Ising model with convolutional neural networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(261).pdf" style="text-decoration:none;">Quantum Convolutional Neural Networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(262).pdf" style="text-decoration:none;">Quantum Neural Network and Soft Quantum Computing</a></li> 
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(263).pdf" style="text-decoration:none;">Learning multiple order parameters with interpretable machines</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(264).pdf" style="text-decoration:none;">Nonequilibrium fluctuations of a driven quantum heat engine via machine learning</a></li> 
 
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(265).pdf" style="text-decoration:none;">Topographic Representation for Quantum Machine Learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(266).pdf" style="text-decoration:none;">Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation</a></li> 
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(267).pdf" style="text-decoration:none;">Thermodynamics and Feature Extraction by Machine Learning</a></li>
 
  
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(268).pdf" style="text-decoration:none;">A jamming transition from under- to over-parametrization affects loss landscape and generalization</a></li>
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(269).pdf" style="text-decoration:none;">Emulating quantum computation with articial neural networks</a></li>
  
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(270).pdf" style="text-decoration:none;">The Role of Data in Model Building and Prediction: A Survey Through Examples</a></li>
 
 
 
  <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(271).pdf" style="text-decoration:none;">Toward an AI Physicist for Unsupervised Learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(272).pdf" style="text-decoration:none;">Reconstructing quantum states with generative models</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(273).pdf" style="text-decoration:none;">Quantum data compression by principal component analysis</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(274).pdf" style="text-decoration:none;">Free energies of Boltzmann Machines: self-averaging, annealed and replica symmetric approximations in the thermodynamic limit</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(275).pdf" style="text-decoration:none;">Machine learning density functional theory for the Hubbard model</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(276).pdf" style="text-decoration:none;">Quantum advantage in training binary neural networks</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(277).pdf" style="text-decoration:none;">Quantum-inspired classical algorithms for principal component analysis and supervised clustering</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(278).pdf" style="text-decoration:none;"> Deep Learning of Robust and High-Precision Quantum Controls </a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(279).pdf" style="text-decoration:none;">Machine learning for molecular dynamics with strongly correlated electrons</a></li>
  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(280).pdf" style="text-decoration:none;">An Artificial Neuron Implemented on an Actual Quantum Processor </a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(281).pdf" style="text-decoration:none;">Artificial neural networks for density-functional optimizations in fermionic systems</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(282).pdf" style="text-decoration:none;">Adversarial Uncertainty Quantification in Physics-Informed Neural Networks</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(283).pdf" style="text-decoration:none;">Efficient prediction of 3D electron densities using machine learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(284).pdf" style="text-decoration:none;">Neural network state estimation for full quantum state tomography</a></li>
                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(285).pdf" style="text-decoration:none;">Experimental Simultaneous Learning of Multiple Non-Classical Correlations</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(286).pdf" style="text-decoration:none;">Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(287).pdf" style="text-decoration:none;">Advances in Quantum Reinforcement Learning</a></li>   
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(288).pdf" style="text-decoration:none;">Energy Levels of One Dimensional Anharmonic Oscillator via Neural Networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(289).pdf" style="text-decoration:none;">Deep Learning and Density Functional Theory</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(290).pdf" style="text-decoration:none;">fPINNs: Fractional Physics-Informed Neural Networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(291).pdf" style="text-decoration:none;">Physics-aware Deep Generative Models for Creating Synthetic Microstructures</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(292).pdf" style="text-decoration:none;">Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning</a></li> 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(293).pdf" style="text-decoration:none;">Estimating of the inertial manifold dimension for a chaotic attractor of complex Ginzburg-Landau equation using a
neural network</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(294).pdf" style="text-decoration:none;">Quantum error correction for the toric code using deep reinforcement learning</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(295).pdf" style="text-decoration:none;">Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations</a></li>                              
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(296).pdf" style="text-decoration:none;">Variational optimization in the AI era: Computational Graph States and Supervised Wave-function Optimization</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(297).pdf" style="text-decoration:none;">Classifying Snapshots of the Doped Hubbard Model with Machine Learning</a></li>
   
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(298).pdf" style="text-decoration:none;">Designing neural network based decoders for surface codes</a></li>
 
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(299).pdf" style="text-decoration:none;">Quantum topology identification with deep neural networks and quantum walks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(300).pdf" style="text-decoration:none;">Divergence of predictive model output as indication of phase transitions</a></li>
 
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(301).pdf" style="text-decoration:none;">Designing quantum experiments with a genetic algorithm</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(302).pdf" style="text-decoration:none;">Phase transition encoded in neural network</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(303).pdf" style="text-decoration:none;">Approximating the solution to wave propagation using deep neural networks</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(304).pdf" style="text-decoration:none;">Boltzmann Generators – Sampling Equilibrium States of Many-Body Systems with Deep Learning</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(305).pdf" style="text-decoration:none;">Enhancing the efficiency of quantum annealing via reinforcement: A path-integral Monte Carlo simulation of the quantum
reinforcement algorithm</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(306).pdf" style="text-decoration:none;">Quantum algorithms for feedforward neural networks</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(307).pdf" style="text-decoration:none;">A hybrid machine-learning algorithm for designing quantum experiments</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(308).pdf" style="text-decoration:none;">q-means: A quantum algorithm for unsupervised machine learning </a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(309).pdf" style="text-decoration:none;">Adaptive Quantum State Tomography with Neural Networks</a></li>
  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(310).pdf" style="text-decoration:none;">Machine Learning as a universal tool for quantitative investigations of phase transitions </a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(311).pdf" style="text-decoration:none;">Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(312).pdf" style="text-decoration:none;">Parameters optimization and real-time calibration of Measurement-Device-Independent Quantum Key Distribution Network based on BackPropagation Artificial Neural Network</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(313).pdf" style="text-decoration:none;">Optimizing Quantum Error Correction Codes with Reinforcement Learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(314).pdf" style="text-decoration:none;">Deep ToC: A New Method for Estimating the Solutions of PDEs</a></li>
                              
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(315).pdf" style="text-decoration:none;">Variational quantum simulation of general processes</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(316).pdf" style="text-decoration:none;">QuCumber: wavefunction reconstruction with neural networks</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(317).pdf" style="text-decoration:none;">Quantum algorithm and quantum circuit for A-Optimal Projection: dimensionality reduction</a></li>   
  
<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(318).pdf" style="text-decoration:none;">Extrapolation of quantum observables with Gaussian processes</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(319).pdf" style="text-decoration:none;">The geometry of quantum learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/Physics-ML-Papers/blob/master/pyl(320).pdf" style="text-decoration:none;">Improved Bounds on Quantum Learning Algorithms</a></li>

</ul>

</br>
 
 
 
<h2>Lecture Notes </h2>

<ul>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(1).pdf" style="text-decoration:none;">Exercise Sheet 1</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(2).pdf" style="text-decoration:none;">Exercise Sheet 2</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(3).pdf" style="text-decoration:none;">Exercise Sheet 3</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(4).pdf" style="text-decoration:none;">Exercise Sheet 4</a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(5).pdf" style="text-decoration:none;">Introduction</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(6).pdf" style="text-decoration:none;">Linear regression</a></li>
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(7).pdf" style="text-decoration:none;">Maximum Likelihood</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(8).pdf" style="text-decoration:none;"> Nonlinear ridge regression
Risk, regularization, and cross-validation </a></li>
   <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(9).pdf" style="text-decoration:none;">Optimization</a></li>
  
   
 <li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(10).pdf" style="text-decoration:none;">Logistic regression: a simple ANN </a></li>                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(11).pdf" style="text-decoration:none;">Backpropagation: A modular approach
(Torch NN)</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(12).pdf" style="text-decoration:none;">Neural networks</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(13).pdf" style="text-decoration:none;">Convnets</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(14).pdf" style="text-decoration:none;">Max-margin learning</a></li>
                              
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(15).pdf" style="text-decoration:none;">Recurrent nets and LSTM</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(16).pdf" style="text-decoration:none;">Reinforcement learning</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(17).pdf" style="text-decoration:none;">Introduction to Lua and Torch</a></li>   
  
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(18).pdf" style="text-decoration:none;">Linear models</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(19).pdf" style="text-decoration:none;">Classifying digits and tuning optimizers</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(20).pdf" style="text-decoration:none;">Implementing your own layer</a></li>

<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(21).pdf" style="text-decoration:none;">nngraph</a></li>
<li><a target="_blank" href="https://github.com/manjunath5496/AI-Lecture-Notes/blob/master/oxf(22).pdf" style="text-decoration:none;">Practical 6: LSTMs for language modelling</a></li> 
 </ul>

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