# Columbia STAT 8201: Deep Generative Models

## Course Introduction

- **University**: Columbia University
- **Prerequisites**: Machine Learning, Deep Learning, Graphical Models
- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
- **Course Website**: [STAT 8201](http://stat.columbia.edu/~cunningham/teaching/GR8201/)

"Deep Generative Models" is a Ph.D. level seminar course at Columbia University, taught by John Cunningham. This course is structured around weekly paper presentations and discussions, focusing on deep generative models, which represent the intersection of graphical models and neural networks and are one of the most important directions in modern machine learning.

The course is designed to explore the latest advancements and theoretical foundations in deep generative models. Participants engage in in-depth discussions about current research papers, fostering a deep understanding of the subject matter. This format not only helps students keep abreast of the latest developments in this rapidly evolving field but also sharpens their critical thinking and research skills.

Given the advanced nature of the course, it is ideal for Ph.D. students and researchers who have a solid foundation in machine learning, deep learning, and graphical models, and are looking to delve into the cutting-edge of deep generative models. The course website provides a valuable resource for accessing the curriculum and related materials.