Lecture materials for the Imperial College London course on Probabilistic Inference, in the Department of Computing. Lecture recordings are available on Panopto.
I welcome any suggestions and fixes from students. If you want to make a contribution, please fork the repo, and make a pull request. If you make a fix, you can claim a chocolate bar at a lecture.
Overview:
- L1: Course overview
- L1: Building Probabilistic Models
- L2: Graphical models
Gaussian Processes and the Behaviour of Bayesian Inference
- L3: Priors on functions
- L3: From Linear Models to Gaussian Processes
- L4: Gaussian Processes
- L5: Model Selection
- L6: Marginal Likelihood
- L6: GP Limitations & Challenges
From Beliefs to Actions
- L7: Decision Theory
- L8: Bayesian Optimisation
Approximate Inference
- L9: Conjugate and Non-Conjugate Models
- L9: Logistic Regression
- L9: Monte Carlo
- L10: Markov Chain Monte Carlo
- L11: Variational Inference
- L12: Stochastic & Amortised Variational Inference, VAEs
The Present and the Future
- L13: Diffusion Models
- L14: Epilogue (non-examinable)