The purpose of these notes is to help understand the intuition and mathematics behind some of the most common machine learning algorithms. An attempt has been made to make each document self-contained. Basic knowledge of probability and statistics is assumed.
Supervised learning
- AdaBoost
- Backpropagation
- Decision trees
- Fisher's Linear Discriminant Analysis
- Linear Regression
- Logistic Regression
- Naive Bayes
- Support Vector Machines
- The Perceptron
Unsupervised learning
- Hidden Markov Models
- Independent Component Analysis
- Principal Component Analysis
- Singular Value Decomposition
- Please raise any issues here.