Here are my revision notes for this degree. Understanding and learning these summary notes alone got me a distinction in my exams, so hopefully they're mostly correct and somewhat thorough. These notes are definitely not perfect and messily hand written, but maybe someone will find something useful.
The general structure of my notes is Overview, Summary, Notes (each more detailed than the previous). Some modules may be missing the overview and/or summary if I decided it wasn't necessary.
- COMPGI01 Supervised Learning: Theory behind lots of what you'll find in scikit-learn.
- COMPGI09 Applied ML: Actually not so applied. Overview of some Supervised and Unsupervised Learning approaches and optimisation.
- COMPGI13 Advanced ML: Covers deep learning and reinforcement learning. Each half can be treated independently.
- COMPGI18 Probabilistic & Unsupervised Learning: Basic Bayes, Latent Variables, Expectation Maximisation, Graphical Models and Gaussian Processes.
- STATG001 Statistical Models & Data Analysis: Crash course in classical statistics.
- STATG004 Bayesian Stats: Introduction to Bayesian statistics.
- STATG017 Stochastic Finance 1: Derivative pricing, Stochastic Calculus, Black Scholes, The Greeks.
- STATG020 Stochastic Finance 2: Market Risk, Credit Risk, Asset pricing techniques
- Summary.rst: (Work in progress) Summary of the main areas of statistics and machine learning I have studied.