Probabilistic Foundations of ML

Warning

This course will be offered permanently at Wellesley under a new course number, CS345. Note the course and its prerequisites have changed a little since its first offering in Fall 2024, so be sure to check the Wellesley Course Browser for the most up-to-date information!

Instructor: Yaniv Yacoby (he/they)

Semester: Fall 2024

Course Number: CS 349 @

Description: In recent years, Machine Learning has enabled applications that were previously not thought possible—from systems that propose novel drugs or generate new art/music, to systems that accurately and reliably predict outcomes of medical interventions in real-time. But what has enabled these developments? Faster computing hardware, large amounts of data, and the Probabilistic paradigm of Machine Learning (ML), a paradigm that casts recent advances in ML, like neural networks, into a statistical learning framework. In this course, we introduce the foundational concepts behind this paradigm—statistical model specification, and statistical learning and inference—focusing on connecting theory with real-world applications and hands-on practice. 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 Bayesian regression/classification, generative models (latent variable models) for clustering, dimensionality reduction, and time-series forecasting. Students will get hands-on experience building models for specific tasks, most taken from healthcare contexts, using NumPyro, a Python-based probabilistic programming language.

Meeting Times:

• Mondays, 9:55-11:10am

• Wednesdays, 9:30-10:20am

• Thursdays, 9:55-11:10am

Location: Science Center Hub 401 Classroom

Prerequisites: CS 230 and at least one of MATH 205, MATH 206, or MATH 225. Permission of the instructor is also required.