Mathematics for Machine Learning (Autumn 2021 @ Imperial)

- Gradient descent (GD) [notes]
- constant step-size GD, convergence analysis [slides]
- Advanced techniques & SGD [slides]

- Ridge regression [notes]
- bias-variance trade-off [slides]

- Principal component analysis (PCA) [notes]
- minimum reconstruction error view [slides]
- maximum variance view [slides]

- Constrained optimisation
- The kernel trick [slides][notes]

Deep Learning (Winter 2022 @ Imperial)

- Generative models
- Intro to generative modelling [slides]
- Variational auto-encoders [slides] [notes]
- Generative adversarial networks [slides] [notes]
- Recent advances in deep generative models [slides]

- RNNs
- Attention networks
- Attention basics & Transformer [slides] [notes]
- Recent advances in attention methods [slides]