[H1] Machine Learning Notes

Description

This Note for Machine Learning is used for revision purposes. It contains all lecture+tutorial topics from week 1 to week 12. Note was made using markdown and followed the course structure. Topics include: - Introduction to ML - Different types of ML algorithms - Probability Model - Naive Bayes - Classification Evaluation - Decision Tree - Instance-based Learning - Distance measurement - K nearest neighbour - Support Vector Machine - Attributes and feature selection - Discretisation - Linear regression - Logistic regression - Classifier combination (stacking, boosting, bagging etc.) - Semi-supervised learning - Interpreting models, error analysis - Evaluation - Neural networks - Deep learning - Sequential Classification - Markov chain - Forward Algorithm - Viterbi Algorithm I used this note to obtain H1 in final exam. All mathematics formula are typed using LaTeX and summarised from lectures. It was very useful and you can easily spend 1 or 2 weeks on it to achieve H1 :)