travisddavies / statistical_machine_learning_notes

My notes for COMP90051 Statistical Machine Learning at University of Melbourne

Statistical Machine Learning

My notes for COMP90051 Statistical Machine Learning at The University of Melbourne.

Note

These notes should be read via Obsidian. The latex and embedded media does not render very well via GitHub.

To get the notes, simply use the following:

git clone [e-mail]:travisddavies/statistical_machine_learning_notes.git

Table of Contents

1. Math Review
2. Statistical Schools of Thought
3. Linear Regression
4. Logistic Regression
5. Regularisation
6. PAC Learning Theory
7. VC Theory
8. Support Vector Machines
9. Kernel Methods
10. The Perceptron
11. Neural Network Fundamentals
12. Training Deep Networks & Autoencoders
13. Convolution Neural Networks
14. Recurrent Neural Networks & Transformers
15. Graph Convolutional Networks
16. Learning With Experts
17. Multi-Armed Bandits
18. Bayesian Regression
19. Bayesian Classification
20. PGM Representation I
21. PGM Representation II
22. Inference on PGMs
23. Gaussian Mixture Models