Course Description

Welcome to the fascinating field of advanced machine learning, a discipline at the intersection of computer science, statistics, and intelligence! Throughout this course, we will explore various advanced machine learning techniques and algorithms from both theoretical and empirical perspectives. Real-world examples and case studies will illustrate the practical applications of advanced machine learning across diverse fields:

• Generative Modeling: Kernel Density Estimation, Gaussian Mixture Models, AE, VAE, GAN, Diffusion Model
• Reinforcement Learning
• Agentic ML
• Neural-Symbolic ML

Students will complete a team-based (optional) course project, a paper presentation, and finish some coding assignments.

Coding notebooks will be provided when necessary for some important topics.

Prerequisite: CS 472/572, CS473/573.

Lectures

Friday, 2:00-3:20 pm, 140 Allen

Office Hours

Instructor: Zoom Meeting or In Class

• Friday 3:20-4:00 pm

• Schedule by Email Request