If you would like to begin studying Machine Learning, in theory approach, we recommend the lecture notes of Prof. Andrew Ng (Stanford University). The chapters are given below:

- Supervised Learning, Discriminative Algorithms [cs229-notes1]
- Generative Algorithms [cs229-notes2]
- Support Vector Machines [cs229-notes3]
- Learning Theory [cs229-notes4]
- Regularization and Model Selection [cs229-notes5]
- Online Learning and the Perceptron Algorithm [cs229-notes6]
- Unsupervised Learning,
*k*-means clustering [cs229-notes7a] - Mixture of Gaussians [cs229-notes7b]
- The EM Algorithm [cs229-notes8]
- Factor Analysis [cs229-notes9]
- Principal Components Analysis (PCA) [cs229-notes10]
- Independent Components Analysis [cs229-notes11]
- Reinforcement Learning and Control [cs229-notes12]