Probabilistic Foundations of ML

Yaniv Yacoby (he/him)	Sally Kim (she/her)	Caroline Jung (she/her)
Instructor	Teaching Assistant	Teaching Assistant
Assistant Professor Computer Science	Math & Computer Science Major Class of 2025	Data Science Major Class of 2025

Semester: Fall 2024

Course Number: CS 349 @

Description: In recent years, Artificial Intelligence 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. This course lays the foundation for advanced study and research in Machine Learning. 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 a probabilistic programming language based in Python.

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.