Resources

Bayesian Deep Learning (Term 2, 25-26)

New Bayesian Deep Learning book coming out soon. Untill then you can check the following reading lists.

• Bayesian Learning and Neural Networks
• Chin-Wei Huang’s reading list

Optimisation for ML (Term 1, 25-26)

Textbooks

• Algorithms for Optimization
• Lectures on Optimization
• Numerical Optimisation

Geometric Deep Learning (Term 3, 24-25)

Papers and Surveys

• Graph Neural Networks: A Review of Methods and Applications
• Statistical exploration of the Manifold Hypothesis

Textbooks

• Graph Representation Learning
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Lecture Series

• CS224W: Machine Learning with Graphs

Reinforcement Learning (Term 2, 24-25)

Papers and Surveys

• AlphaGo
• AlphaFold
• Reinforcement Learning: An Overview

Textbooks

• Reinforcement Learning: An Introduction

Lecture Series

• DeepMind x UCL | Deep Learning Lecture Series 2021
• CS285 Deep Reinforcement Learning

Diffusion Models (Term 1, 24-25)

Tutorials

• Step-by-Step Diffusion: An Elementary Tutorial
• Denoising Diffusion Probabilistic Models in Six Simple Steps
• Tutorial on Diffusion Models for Imaging and Vision

Papers and Surveys

• Score-Based Generative Modeling through Stochastic Differential Equations
• Diffusion Models: A Comprehensive Survey of Methods and Applications
• High-Resolution Image Synthesis with Latent Diffusion Models
• A Unified Framework for U-Net Design and Analysis

Blogs and Videos

• Song Yang’s Blog Post on Diffusion
• Diffusion Models | PyTorch Implementation
• Diffusion Models | Paper Explanation | Math Explained
• Score-based Generative Modeling in Latent Space

General Machine Learning Textbooks

• Probabilistic Machine Learning 1 - Kevin Murphy
• Probabilistic Machine Learning 2 - Kevin Murphy
• Mathematics for Machine Learning
• Deep Learning Foundations and Concepts - Chris and Hugh Bishop