Imperial College London (CO-493)
Course Outline
- Graphical models
- Gaussian processes
- Bayesian optimization
- Logistic regression
- Sampling
- Variational inference
- Sparse Gaussian processes (slides by Hugh Salimbeni)
Other Material
Lectures
- Tuesdays, 16:00 - 18:00 (Huxley, 308)
- Fridays, 11:00 - 13:00 (Huxley, 340)
Tutorials
- Gaussian Processes (ipynb)
- Bayesian Optimization (ipynb)
- Variational Inference (ipynb)
Coursework
- Coursework 1: Gaussian processes
- Coursework 2: Logistic regression and MCMC
- Coursework 3: Variational inference
Test
Graphical models
References
- Bishop: Pattern Recognition and Machine Learning
- Rasmussen & Williams: Gaussian Processes for Machine Learning
- Deisenroth et al.: Mathematics for Machine Learning (background reading)
Teaching Assistants
- Ian Walker
- Kenneth Co
- Linh Tran
- Nick Pawlowski
- Sanket Kamthe
- Steindor Saemundsson