COMPSCI 389: Introduction to Machine Learning

Spring 2026, University of Massachusetts

Lecture: Tuesdays and Thursdays, 1-2:15 in the Computer Science Building, Room 142

Course Information

Download Syllabus .pdf

Description

The course provides an introduction to machine learning algorithms and applications. Machine learning algorithms answer the question: "How can a computer improve its performance based on data and from its own experience?" The course is roughly divided into thirds: supervised learning (learning from labeled data), reinforcement learning (learning via trial and error), and real-world considerations like ethics, safety, and fairness. Specific topics include linear and non-linear regression, (stochastic) gradient descent, neural networks, backpropagation, classification, Markov decision processes, state-value and action-value functions, temporal difference learning, actor-critic algorithms, the reward prediction error hypothesis for dopamine, connectionism for philosophy of mind, and ethics, safety, and fairness considerations when applying machine learning to real-world problems.

Office Hours

See the syllabus for complete information, including TA contact information. Below is a summary of office hour times and locations for quick reference.

  • Mondays, TBD.
  • Tuesdays, TBD.
  • Wednesdays, TBD.
  • Thursdays, TBD.
  • Fridays, TBD.

Instructions and Guides

Note: To display .ipynb files, right click the link and select "Save Link As" (Firefox), or a similar option in your browser. Then open the downloaded file using VSCode.

Title Date Description Document Link
Jupyter Notebook Installation N/A Instructions describing how to install Python, VSCode, and set them up to work with .ipynb (Jupyter Notebook) files on MacOS and Windows. instructions (.pdf), instructions (.md)
Jupyter Notebook Introduction N/A An example Jupyter Notebook explaining how notebooks work and providing a very basic introduction to Python. notebook (.ipynb)

Homework Assignments

Note: Homework assignments should be submitted in Gradescope.
Assignment Number Date Assigned Date/Time Due Document Link

Lecture Slides and Code Notebooks

Lecture Lecture Date Topic Document Link
Lecture #1, Part 1 January 29, 2026 Course Introduction slides (.pdf)
Lecture #1, Part 2 January 29, 2026 Introduction to ML slides (.pdf)
Lecture #2 February 3, 2026 Introduction to Supervised Learning and Data Sets slides (.pdf)
Lecture #3 February 5, 2026 Data Analysis slides (.pdf), notebook (.ipynb)