Watermelon book
1. Introduction
2. Model evaluation and selection
3. Linear model
4. Decision tree
5. Neural network
6. SVM
7. Bayesian classifier
8. Ensemble learning
9. Clustering
10. Dimensionality reduction and metric learning
11. Feature selection And sparse learning
12. Computational learning theory
13. Semi-supervised learning
14. Probability graph
15. Rule learning
16. Reinforcement learning
Statistical learning methods
5. Decision tree
6. Logistic regression and maximum entropy
7. Support vector machine
8.
Boosting method 9. EM
10. Hidden Markov
11. Conditional random field
12. Supervised learning summary
13. Unsupervised learning introduction
14. Clustering method
15. Singular Value Decomposition
16. Principal Component Analysis
17. Latent Semantic Analysis
18. Probabilistic Latent Semantic Analysis
19. Markov Chain Monte Carlo
20. Latent Dirichlet
21. PageRank
22. Summary of Unsupervised Learning
A. Gradient Descent
B .Newton 's method and quasi-Newton
C. Lagrangian duality
D. Matrix elementary subspace
E. KL divergence
Machine learning in action
2. k nearest neighbors
3. decision tree
4. Bayes
5. logistic regression
6.
svm
7. adaboost 8. regression
9. tree regression
10.
kmeans
11.apriori 12. fp-growth
13.
pca
14. svd 15.map -reduce
Whiteboard derivation
(Series 2) Mathematical Fundamentals-Probability
(Series 3) Linear Regression
(Series 4) Linear Classification
(Series 5) Dimensionality Reduction
(Series 6) Support Vector Machine
(Series 7) Kernel Method
(Series 8) Exponential Family Distribution
(Series 9) Probabilistic Graphical Model
(Series Ten) EM Algorithm
(Series Eleven) Gaussian Mixture Model
(Series Twelve) Variational Inference
(Series Thirteen) MCMC
(Series Fourteen) Hidden Markov Model
(Series Fifteen) Linear Dynamic System
( Series 16) Particle Filter
(Series 17) Conditional Random Field
(Series 18) Gaussian Network
(Series 19) Bayesian Linear Regression
(Series 20) Gaussian Process Regression
(Series 21) Restricted Boltz Man machine
(series 22) spectral clustering
(series 23) feedforward neural network
Neural Networks and Deep Learning-Qiu Xipeng
Chapter 4 Feedforward Neural Networks
Distributed machine learning: algorithms, theory and practice
Chapter 3 Distributed Machine Learning Framework