Machine Learning Seminar (2024/25)

University College London (COMP0168)

This course is designed to introduce students to “trending” topics within the last five years as represented in international machine learning conferences. The backbone of the course will be a series of tutorial-style introductory lectures on a given set of selected topics. This will be supplemented by seminar-style course work, where current research papers are read, reviewed, presented, and discussed.

Syllabus

1. Gaussian Processes (please use Acrobat Reader for the animations) — Marc Deisenroth
2. Bayesian Optimization — Marc Deisenroth
3. Modern Integration in Machine Learning — Marc Deisenroth
   • Introduction
   • Numerical Integration
   • Monte Carlo Integration
   • Normalizing Flows
   • Inference in Time Series
4. Message Passing in Machine Learning — Brooks Paige
5. Meta Learning — Brooks Paige

Delivery

The course will be delivered (at least partially) online. Lecture recordings will be available for viewing at home. We will have live Q&A in allocated time slots, if possible on campus.

Teaching Assistants

• Shuotian (Alice) Cheng
• Vincent Abbott
• Ruoqing Yin