Machine Learning Seminar (2023/24)

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 in common, reviewed, discussed, and presented.

Syllabus (preliminary)

  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
  4. Message Passing in Machine Learning — Brooks Paige
  5. Meta Learning — Brooks Paige


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

  • Shyam Sundhar Ramesh
  • Beatrice Taylor
  • Shuotian (Alice) Cheng


Gaussian Processes

Bayesian Optimization

Integration in Machine Learning

Message Passing in Machine Learning

Meta Learning