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cs-roadmap

★ 47 Open GitHub ↗

My Computer Science Curriculum

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## Computer Science Roadmap

- [x] [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn)

### Intro to CS

- [x] [CS50 - Introduction to Computer Science - Harvard](cs50.tv/)
- [x] [6.0001 - Introduction to Computer Science and Programming in Python, Fall 2016 - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/)
- [x] [6.0002 - Introduction to Computational Thinking and Data Science, Fall 2016 - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/)

### Programming

- [x] [CS61A - Structure and Interpretation of Computer Programs (Python + Scheme) - UC Berkeley](http://cs61a.org/)
- [ ] [CS61A - Structure and Interpretation of Computer Programs (Scheme), 2010 - UC Berkeley](https://www.youtube.com/playlist?list=PLhMnuBfGeCDNgVzLPxF9o5UNKG1b-LFY9)
- [ ] [CS106A - Programming Methodology (Java) - Stanford](https://see.stanford.edu/Course/CS106A)
- [ ] [CS106B - Programming Abstractions (C++) - Stanford](https://see.stanford.edu/Course/CS106B)
- [ ] [CS107 - Programming Paradigms - Stanford](https://see.stanford.edu/Course/CS107)
- [ ] [CSE341 - Programming Languages, Spring 2013 - University of Washington](https://courses.cs.washington.edu/courses/cse341/13sp/)
- [ ] [CS212 - Design of Computer Programs - Peter Norvig](https://eu.udacity.com/course/design-of-computer-programs--cs212)
- [ ] [CS210 - Functional Programming in Scala - EPFL](https://www.coursera.org/specializations/scala)

### Maths

- **Calculus**
    - [x] [18.01 - Single Variable Calculus, Fall 2006 - MIT](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/)
    - [x] [18.02 - Multivariable Calculus, Fall 2007 - MIT](https://ocw.mit.edu/courses/mathematics/18-02-multivariable-calculus-fall-2007/)
    - [ ] [18.03 - Differential Equations, Spring 2010 - MIT](https://ocw.mit.edu/courses/mathematics/18-03-differential-equations-spring-2010/)

- **Linear Algebra**
    - [x] [18.06 - Linear Algebra, Fall 2011 - MIT](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/)
    - [ ] [Linear Algebra Review - CMU](http://www.cs.cmu.edu/~zkolter/course/linalg/index.html)

- **Probability and Statistics**
    - [x] [6.041 - Probabilistic Systems Analysis and Applied Probability, Fall 2013 - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013/)
    - [ ] [STAT110 - Probability - Harvard](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo)
    - [ ] [18.650 - Statistics for Applications, Fall 2016 - MIT](https://ocw.mit.edu/courses/mathematics/18-650-statistics-for-applications-fall-2016/)
    - [ ] [36-705 - Intermediate Statistics, Fall 2016 - CMU](http://www.stat.cmu.edu/~larry/=stat705/)
    - [ ] [6.262 - Discrete Stochastic Processes, Spring 2011 - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-262-discrete-stochastic-processes-spring-2011/)
    - [ ] [AM207 - Stochastic Methods for Data Analysis, Inference and Optimization, 2016 - Harvard](http://am207.github.io/2016/index.html)

-  **Discrete Maths**
    - [x] [6.042J - Mathematics for Computer Science, Fall 2010 - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/)

-  **Opmitisation**
    - [ ] [EE364A - Convex Optimization I - Stanford](https://see.stanford.edu/Course/EE364A)
    - [ ] [EE364B - Convex Optimization II - Stanford](https://see.stanford.edu/Course/EE364B)
    - [ ] [10-725 - Convex Optimization, Fall 2016 - CMU](http://www.stat.cmu.edu/~ryantibs/convexopt/)
    - [ ] [MOOC - Discrete Optimization](https://www.coursera.org/learn/discrete-optimization)

- **Maths for ML (mostly books)**
    - [ ] [10-606 - Math Background for Machine Learning, Fall 2017 - CMU](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA)
    - [ ] [18-657 - Mathematics of Machine Learning, Fall 2015 - MIT](https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/)
    - [ ] [CO-496 - Mathematics for Inference and Machine Learning - Imperial College](http://wp.doc.ic.ac.uk/sml/teaching/mathematics-for-machine-learning-autumn-2016/)
    - [ ] [Book - Mathematics for Machine Learning - Imperial College](https://mml-book.com/)
    - [ ] [Book - Mathematics for Machine Learning - UC Berkeley](http://gwthomas.github.io/docs/math4ml.pdf)

- **Other**
    - [ ] [MOOC - Introduction to Logic - Stanford](https://www.coursera.org/learn/logic-introduction/)
    - [ ] [18.S096 - Topics in Mathematics with Application in Finance, Fall 2013 - MIT](https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/)
    - [ ] [MOOC - Game Theory - Stanford](https://www.coursera.org/learn/game-theory-1)

### Data Structures and Algorithms

- [ ] [CS61B - Data Structures, Spring 2019 - UC Berkeley](https://sp19.datastructur.es/)
- [ ] [6.006 - Introduction to Algorithms, Fall 2011 - MIT](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/)
- [ ] [COS226 - Algorithms - Princeton](https://algs4.cs.princeton.edu/home/)
- [ ] [6.046J - Design and Analysis of Algorithms, Spring 2015 - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/)
- [ ] [CS161 - Algorithms: Design and Analysis, Part 1 - Stanford](https://lagunita.stanford.edu/courses/course-v1:Engineering+Algorithms1+SelfPaced/about)
- [ ] [CS161 - Algorithms: Design and Analysis, Part 2 - Stanford](https://lagunita.stanford.edu/courses/course-v1:Engineering+Algorithms2+SelfPaced/about)
- [ ] [6.851 - Advanced Data Structures, Spring 2012 - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-851-advanced-data-structures-spring-2012/)
- [ ] [CS224 - Advanced Algorithms, Fall 2014 - Harvard](http://people.seas.harvard.edu/~minilek/cs224/fall14/index.html)
- [ ] [CS229R - Algorithms for Big Data, Fall 2015 - Harvard](http://people.seas.harvard.edu/~minilek/cs229r/fall15/index.html)

### Computer Architecture

- [ ] [CS61C - Great Ideas in Computer Architecture, Spring 2015 - UC Berkeley](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iCl2-D-FS5mk0jFF6cYSJs_)
- [ ] [CS152 - Computer Architecture and Engineering, Spring 2016 - UC Berkeley](https://www.youtube.com/playlist?list=PLkFD6_40KJIwEiwQx1dACXwh-2Fuo32qr)
- [ ] [18-447 - Computer Architecture, Spring 2015 - CMU](http://www.archive.ece.cmu.edu/~ece447/s15/doku.php?id=schedule)
- [ ] [15-418 - Parallel Computer Architecture and Programming, Spring 2016 - CMU](https://www.youtube.com/playlist?list=PLpIxOj-HnDsO4Atvrp86c-4La9Mq3kMQZ)

### System Programming

- [ ] [15-213 - Introduction to Computer Systems, Fall 2015 - CMU](https://scs.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx#folderID=%22b96d90ae-9871-4fae-91e2-b1627b43e25e%22)
- [ ] [CS162 - Operating Systems and System Programming, Spring 2015 - UC Berkeley](http://www.infocobuild.com/education/audio-video-courses/computer-science/cs162-spring2015-berkeley.html)
- [ ] [6.824 - Distributed Systems, Spring 2015 - MIT](https://www.youtube.com/playlist?list=PLpl804R-ZwjKCOwWpTZ21eeaBS3uBrMfV)

### Software Engineering

- [ ] [CS169 - Software Engineering, Spring 2015 - UC Berkeley](http://www.infocobuild.com/education/audio-video-courses/computer-science/cs169-spring2015-berkeley.html)
- [ ] [CS6310 - Software Architecture & Design - Georgia Tch](https://eu.udacity.com/course/software-architecture-design--ud821)

### Database Systems

- [x] [CS145 - Introduction to Databases - Stanford](https://lagunita.stanford.edu/courses/Home/Databases/Engineering/about)
- [ ] [CS186 - Introduction to Database Systems, Spring 2015 - UC Berkeley](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iBVK2QzAV-R7NMA1ZkaiR2y)
- [ ] [15-445 - Introduction to Database Systems, Fall 2017 - CMU](https://15445.courses.cs.cmu.edu/fall2017/)
- [ ] [15-721 - Advanced Database Systems, Spring 2018 - CMU](https://15721.courses.cs.cmu.edu/spring2018/)

### Computer Networks

- [ ] [14-740 - Fundamentals of Computer Networks, Fall 2017 - CMU](http://www.ini740.com/F17/index.html)
- [ ] [CS144 - Introduction to Computer Networking - Stanford](https://lagunita.stanford.edu/courses/Engineering/Networking-SP/SelfPaced/about)

### Programming Languages

- [ ] [CS143 - Compilers, Fall 2014 - Stanford](https://lagunita.stanford.edu/courses/Engineering/Compilers/Fall2014/about)
- [ ] [CS164 - Programming Languages and Compilers, Spring 2012 - UC Berkeley](http://www.infocobuild.com/education/audio-video-courses/computer-science/cs164-spring2012-berkeley.html)

### Theoretical CS

- [ ] [15-251 - Great Ideas in Theoretical Computer Science - CMU](https://www.youtube.com/playlist?list=PLm3J0oaFux3aafQm568blS9blxtA_EWQv)
- [ ] [CS154 - Automata Theory - Stanford](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+Automata+SelfPaced/about)
- [ ] [Category Theory, Summer 2016](https://www.youtube.com/playlist?list=PLbgaMIhjbmEnaH_LTkxLI7FMa2HsnawM_)

### Machine Learning and Artificial Intelligence

- **Artificial Intelligence**
    - [ ] [CS188 - Introduction to Artificial Intelligence, Fall 2018 - UC Berkeley](https://inst.eecs.berkeley.edu/~cs188/fa18/)
    - [ ] [6.034 - Artificial Intelligence, Fall 2010 - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/)
    - [ ] [15-780 - Graduate Artificial Intelligence, Spring 2017 - CMU](https://www.youtube.com/playlist?list=PLpIxOj-HnDsPfw9slkk0BfwuiNEYVnsd_)

- **Machine Learning**
    - [ ] [STATS216 - Statistical Learning, Winter 2016 - Stanford](https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about)
    - [ ] [CS229 - Machine Learning - Stanford](https://see.stanford.edu/course/cs229)
    - [ ] [CS155 - Machine Learning & Data Mining, Winter 2017 - Caltech](http://www.yisongyue.com/courses/cs155/2017_winter/)
    - [ ] [CS156 - Learning from Data, Caltech](https://work.caltech.edu/lectures.html)
    - [ ] [10-601 - Introduction to Machine Learning (MS), Spring 2015 - CMU](http://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml)
    - [ ] [10-701 - Introduction to Machine Learning (PhD), Spring 2011 - CMU](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml)
    - [ ] [Machine Learning, Fall 2014 - University of Oxford](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
    - [ ] [10-702 - Statistical Machine Learning, Spring 2015 - CMU](https://www.youtube.com/playlist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r)
    - [ ] [Information Theory, Pattern Recognition, and Neural Networks, 2012 - Cambridge](http://www.inference.org.uk/itprnn/)
    - [ ] [CS189/281A - Introduction to Machine Learning, Spring 2016 - UC Berkeley](https://people.eecs.berkeley.edu/~jrs/189s16/)
    - [ ] [C281B - Scalable Machine Learning, 2012 - UC Berkeley](http://alex.smola.org/teaching/berkeley2012/syllabus.html)
    - [ ] [STA4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto](http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/lectures.html)
    - [ ] [18.409 - Algorithmic Aspects of Machine Learning, Spring 2015 - MIT](https://www.youtube.com/playlist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx)
    - [ ] [9.520 - Statistical Learning Theory and Applications, Fall 2015 - MIT](https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O)

- **Deep Learning**
    - [ ] [CSC321 - Neural Networks for Machine Learning - University of Toronto](https://www.coursera.org/learn/neural-networks)
    - [ ] [CS230 - Deep Learning - Stanford](https://www.coursera.org/specializations/deep-learning)
    - [ ] [CS231N - Convolutional Neural Networks for Visual Recognition, Spring 2017 - Stanford](http://cs231n.stanford.edu/2017/)
    - [ ] [CS224N - Natural Language Processing with Deep Learning, Winter 2017 - Stanford](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6)

- **Reinforcement Learning**
    - [ ] [CS294 - Deep Reinforcement Learning, Fall 2018 - UC Berkeley](http://rail.eecs.berkeley.edu/deeprlcourse/)
    - [ ] [COMPM050 - Reinforcement Learning, 2015 - UCL](http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html)

- **Probabilistic Graphical Models**
    - [ ] [10-708 - Probabilistic Graphical Models, Spring 2014 - CMU](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
    - [ ] [CS228 - Probabilistic Graphical Models - Stanford](http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=ProbabilisticGraphicalModels)

- **Data Mining**
    - [ ] [CS246 - Mining of Massive Datasets - Stanford](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+MMDS+SelfPaced/about)
    - [ ] [MOOC - Data Mining - University of Illinois](https://www.coursera.org/specializations/data-mining)
    - [ ] [CS224W - Social and Information Network Analysis, Fall 2017 - Stanford](http://snap.stanford.edu/class/cs224w-videos-2017/)
    - [ ] [MOOC - Recommender Systems - University of Minnesota](https://www.coursera.org/specializations/recommender-systems)

### Other Topics

- **Data Engineering**

- [ ] [MOOC - Big Data for Data Engineering - Yandex](https://www.coursera.org/specializations/big-data-engineering)
    - [ ] [MOOC - Cloud Computing - University of Illinois](https://www.coursera.org/specializations/cloud-computing)
    - [ ] [MOOC - Big Data - UC San Diego](https://www.coursera.org/specializations/big-data)

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