Welcome to the Fall 2023 offering of CSE 6243!

Prerequisite: Graduate level Machine Learning, Probability and Statistics, Numerical Linear Algebra

This class is best for you if you have machine learning at the CORE of your studies/research, and want to understand the fundamentals.

Advanced Machine Learning is a graduate level course introducing the modern machine learning techniques, including graphical models, optimization, generative models, reinforcement learning, etc. These topics will be covered in Four Modules:

  • Module I: Background Knowledge
    • Convex Optimization: Convex function, duality, stochastic gradient descent
    • PGM: directed/undirected graphical models
    • Sampling: MH, Gibbs, HMC
  • Module II: Generative Model
    • VAE, Autoregressive Model, GAN, EBM (CD, Score Matching), Diffusion Models
  • Module III: Differentiable Programming
    • Differentiable Bilevel Optimization, Differentiable Sampler, Differentiable Algorithm (Planning).
  • Module IV: Reinforcement Learning
    • MDP, Dynamic Programming, Policy-gradient, LP-based algorithm
    • (Offline RL, Exploration)

The course will be very math and theory heavy. The students are expected to develop the skill of design computationally and statistically efficient machine learning algorithms for practical problems.

Time: Monday/Wednesday 5:00-06:15 pm
Location: Scheller College of Business 203
Discussion & HW submission: Canvas and Ed Discussion
Contact: Please ask all course-related questions on Ed Discussion, where you will also find announcements. For external enquiries, personal matters, or in emergencies, you can email us at bodai at cc.gatech.edu and haotian.sun at gatech.edu.