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DS 3001: Foundations of Machine Learning

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# DS 3021: Foundations of Machine Learning

Your Foundations of Machine Learning Tour Guides:

* Brian Wright, Ph.D., Office: TBD
* Email: brianwright@virginia.edu
* TA: 
* Class Location: Dell 1 Room 105

Brian's Office Hours: In person in my office SDS Building 434 or Virtual on Discord - Monday 1-3pm

TA office hours: TBD

Course Materials: [Foundations of Machine Learning Repo](https://github.com/UVADS/DS-3001/3021)

Subject Area and Catalog Number: Data Science, DS 3021

Year, Term and Time: 2025, Spring, T/Th

Class Title: Foundations of Machine Learning

Level: Undergraduate

Credit Type: Grade (A-F)

## A Little Bit About the Course

What is Data Science, why is it becoming so important and what is needed to be successful in this field? We will explore these questions throughout the course through a variety of topics with data always at the center.  In all things, we focus on creative thinking, not blind implementation. If you cannot answer why you are doing something, not only will you discover no new knowledge but you will also create new problems versus solving them.

The course centers on lab-based work and employs a team-based pedagogy, meaning much of the work in the course can and should be completed in collaboration with your classmates.  Though very applied, we also include theoretical content and will have discussion sessions depending on the topic for any given week.

I am also aware that students come from different backgrounds and have a variety of skill sets that they bring to the class.  There will be plenty of opportunities for those that would like to take advantage of extra discussions during office hours, TA support sessions, and team-based work that is designed in such a way that we can all learn from each other.

Throughout the course, we will endeavor to “live the life of a data scientist” allowing you to not only be directly taught but gain a sense of what it would be like to be working as a data scientist.  You’ll be asked to discover new knowledge and share it with your peers and learn how to use the larger ecosystem of data science to your benefit.

## What you’ll learn along the way

Data Science is incredibly broad and dynamic. The topics below are designed to reinforce this perspective and help you understand the field’s core tenements and what is demanded from practicing data scientists.  The key is for you to gain a sense of the scope of Data Science, what is needed to contribute to the community, and feel comfortable incorporating these techniques into your work moving forward. Specific learning objectives are below:
    
    Gain experience working in teams to solve Data driven problems
    Be able to describe the field of Machine Learning
    Understand computational and mathematical foundations of the methods presented
    Articulate the advantages and disadvantages of selected ML approaches
    Be able to select appropriate ML models given problems and data types
    Understand the importance of and methods for evaluating ML models
    Gain the foundational knowledge necessary to being ML II

The course will move rather quickly and can be demanding at times. However, if we all work together to support each other you’ll be amazed how much you learn at the end of the semester!

## How You’ll Know You Are Learning

On any given week, the course will require reviewing short video lectures and completing readings prior to coming to class.  These lectures and readings will then be implemented in the lab portion of the course which will be conducted during the scheduled class period.  Lab sessions will include a variety of activities but mostly be centered on team-oriented coding assignments.  Students can also use lab sessions to work on mid-term and final projects when needed.

Quizzes (15%) – Short occasional (5 or 6) quizzes, will be auto-graded, so you will get instant 
    feedback. In order to ensure we are all meeting the learning objectives from week to week short 
    quizzes will be given. You will be allowed as many chances to complete the quiz as needed and 
    they will be open note however students are to work independently. 
    
    Labs (60%) – On most weeks we will have in-class labs/assignments. These are designed to allow you 
    to practice the skills being presented in class. While they should be submitted individually you are 
    encouraged to work with your peers as much of the best learning can come from your peers. 
    You’ll need to create publishable markdown documents for every lab and submit them along 
    with the raw code file and link each week (Groups).   
   
    Final projects (25%) – The course will culminate in a final project that will involve 
    working with a dataset of your choice, giving a presentation, submitting well-annotated 
    code to include summary information in report form. This is an open-ended project 
    designed to allow groups to choose a topic of interest from the semester to
    explore deeper and share with the class.
    
## Tech Stack

VS Code/Google Collab/Rivana - You'll need to have the software loaded and ready to go day one, but we will help if needed. 
    Miniconda/Conda - [Mini-Recommended](https://docs.conda.io/en/latest/miniconda.html)
    Github - Almost all course materials (will post on Canvas as needed)
    Canvas - Submission of assignments and class-wide communications
    Docker + Virtual Machines 
    Discord - Low latency comms for groups and class.

**[Discord Invite Click](tbd)**

**[Overview for Install Miniconda and VS Code](https://medium.com/the-researchers-guide/how-to-set-up-python-and-visual-studio-code-ide-for-data-science-161c61f76fe3)**

## Materials That Will Aid in Your Learning:

The books below are essentially a starter Machine Learning Library. I will use all of these references at difference points during the class, but try to use free options. Unfortunately the main book for the class Python Machine Learning with Pytorch and Scikit-Learn is not free. Everything else is either free or can be found for around 15 dollars. There's also references to Python style guides and tutorials.

* A. [Weapons of Math Destruction](https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815)
* B. [Evaluating Machine Learning Models – O’Reilly Digital via UVA Library](https://www.oreilly.com/library/view/temporary-access/) 
* C. [Python Machine Learning with PyTorch and Scikit-Learn, 4th Edition](https://sebastianraschka.com/blog/2022/ml-pytorch-book.html)
* D. [Applied Predictive Learning, Kuh and Johnson](http://appliedpredictivemodeling.com/toc) - Expensive but very good/Also R based
* E. [Machine Learning Engineering](http://www.mlebook.com/wiki/doku.php)- Free PDF Version
* H. [Mathematics for Machine Learning](https://mml-book.github.io/) Free PDF Version

## Schedule of Topics

***NOTE: depending on student interest, the syllabus can be adjusted to accommodate additional topics

## A few Policies that will Govern the Class

Grading Policies: Courses carrying a Data Science subject area use the following grading system: A, A-; B+, B, B-; C+, C, C-; D+, D, D-; F.  The symbol W is used when a student officially drops a course before its completion or if the student withdraws from an academic program of the University.

Grading Scale:

- 93-100 A
 - 90-92 A- 
 - 87-89 B+
 - 83-86 B 
 - 80-82 B- 
 - 77-79 C+ 
 - 73-76 C 
 - 70-72 C- 
 - <70 F

University of Virginia Honor System: All work should be pledged in the spirit of the Honor System at the University of Virginia. The instructor will indicate which assignments and activities are to be done individually and which permit collaboration. The following pledge should be written out at the end of all quizzes, examinations, individual assignments, and papers:  “I pledge that I have neither given nor received help on this examination (quiz, assignment, etc.)”.  The pledge must be signed by the student. For more information, visit www.virginia.edu/honor.

Special Needs:  The University of Virginia accommodates students with disabilities. Any SCPS student with a disability who needs accommodation (e.g., in arrangements for seating, extended time for examinations, or note-taking, etc.), should contact the Student Disability Access Center (SDAC) and provide them with appropriate medical or psychological documentation of his/her condition. Once accommodations are approved, just follow up with me concerning any logistics and implementation of accommodations.  Please try to make accommodations for test-taking at least 14 business days in advance of the date of the test(s). Students with disabilities are encouraged to contact the SDAC: 434-243-5180/Voice, 434-465-6579/Video Phone, 434-243-5188/Fax. Further policies and statements are available at www.virginia.edu/studenthealth/sdac/sdac.html

Technical Support Contacts

Login/Password: scpshelpdesk@virginia.edu
    UVaCollab: collab-support@virginia.edu
    BbCollaborate Support: http://www.tinyurl.com/uvabbc

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