Introduction to Statistical Machine Learning

Part 1. Introduction
1. Statistical Machine Learning

Part 2. Statistics and Probability
2. Random Variables and Probability Distributions
3. Examples of Discrete Probability Distributions
4. Examples of Continuous Probability Distributions
5. Multidimensional Probability Distributions
6. Examples of Multidimensional Probability Distributions
7. Sum of Independent Random Variables
8. Probability Inequalities
9. Statistical Estimation
10. Hypothesis Testing

Part 3. Generative Approach to Statistical Pattern Recognition
11. Pattern Recognition via Generative Model Estimation
12. Maximum Likelihood Estimation
13. Properties of Maximum Likelihood Estimation
14. Model Selection for Maximum Likelihood Estimation
15. Maximum Likelihood Estimation for Gaussian Mixture Models
16. Nonparametric Estimation
17. Bayesian Inference
18. Analytic Approximation of Marginal Likelihood
19. Numerical Approximation of Predictive Distribution
20. Bayesian Mixture Models

Part 4. Discriminative Approach to Statistical Machine Learning
21. Learning Models
22. Least Squares Regression
23. Constrained Least Squares Regression
24. Sparse Regression
25. Robust Regression
26. Least Squares Classification
27. Support Vector Classification
28. Probabilistic Classification
29. Structured Classification

Part 5. Further Topics
30. Ensemble Learning
31. Online Learning
32. Confidence of Prediction
33. Weakly Supervised Learning
34. Transfer Learning
35. Multitask Learning
36. Linear Dimensionality Reduction
37. Nonlinear Dimensionality Reduction
38. Clustering
39. Outlier Detection
40. Change Detection

Part 6. Deep Learning
41. Feedforward Neural Networks
42. Neural Networks with Image Data
43. Neural Networks with Sequential Data
44. Learning from Limited Data
45. Representation Learning
46. Deep Generative Modelling
47. Multimodal Learning