Machine Learning - Table of Contents

In this series of notes, you will will:

• List the principle algorithms used in machine learning and derive their update rules
• Appreciate the capabilities and limitations of current approaches
• Evaluate the ML algorithms
• Use existing implementations of ML algorithms to explore data sets and build models

Table of Contents

1. Introduction
2. Machine Learning Evaluation
3. Nearest Neighbour Methods
4. Neural Networks: Perceptron
5. Elements of Local Optimisation
6. Neural Networks: Perceptron II
7. Perceptron III
8. Multi Layer Neural Networks
9. Convolutional Neural Networks
10. Multi-Layer Neural Networks
11. Linear Models for Regression
12. Decision Trees
13. Support Vector Machines
14. Support Vector Machines II
15. Support Vector Machines III
16. Support Vector Machines IV
17. Markov Decision Processes
18. K-means