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.