Probabilistic Foundations of Predictive ML

Probabilistic Foundations of Predictive ML#

Notice!

We’re working to expand the ML offerings at Wellesley with the introduction of this new course, CS245, in the Spring of 2026. This course will cover a portion of the topics currently covered by CS345, allowing us to expand the topics covered by CS345. Assuming everything goes according to plan, please anticipate that,

  1. CS345 will be different in the Fall of 2026.

  2. CS345 will require CS245 as its only prerequisite.

Instructor: Yaniv Yacoby (he/they)

Semester: Spring 2026

Course Number: CS 245 @ Wellesley CollegeWellesley College

Description: In recent years, Machine Learning (ML) has been used in novel applications—from generating new art and music to systems that accurately and reliably predict outcomes of medical interventions in real-time. Faster computing hardware, large datasets, and the probabilistic paradigm of ML, which frames advances like neural networks within statistical learning, have enabled these developments. In this course, we introduce the foundational concepts behind the probabilistic paradigm of predictive ML: statistical model specification and learning. We will focus on connecting theory with real-world applications. Students will get hands-on experience building models for specific tasks, most taken from healthcare contexts, using probabilistic programming languages. While expanding our methodological toolkit, we will simultaneously introduce critical perspectives to examine the ethics of ML within sociotechnical systems. This course lays the foundation for advanced study and research in ML. Topics include: directed graphical models, deep regression/classification, frequentist learning, and model evaluation.

Textbook: We wrote a specialized textbook especially for this course.

Meeting Times:

  • Mondays 2:20-3:35pm

  • Wednesdays 1:30-2:45pm

  • Thursdays 2:20-3:35pm

Location: TBD

Distributions: Data Literacy, and Mathematical Modeling and Problem Solving.

Prerequisites:

  1. One of the following: CS 230, CS 230P, CS 230X.

  2. MATH 115 or similar prior experience with univariate calculus.

  3. One of the following: MATH 205, MATH 206, MATH 220, MATH 225, STAT 218, or STAT 318.

  4. Comfort in Python.

  5. Permission of the instructor.