Read the summary of "machine learning - Zhou Zhihua"

Start machine learning journey , First, sort out the overall idea of machine learning , Each chapter will be supplemented continuously , Thank you for your support

Chapter one The introduction

1.1 introduction
1.2 Basic terminology
1.3 Hypothetical space
1.4 Generalize preferences
1.5 development history
1.6 Application status
1.7 Reading materials

The first 2 Chapter Model evaluation and selection

2.1 Empirical error and over fitting
2.2 Evaluation methods
2.3 Performance metrics
2.4 Comparative test
2.5 Deviation and variance
2.6 Reading materials

The first 3 Chapter Linear model

3.1 Basic form
3.2 Linear regression
3.3 Log probability regression
3.4 Linear discriminant analysis
3.5 Multi category learning
3.6 Category imbalance
3.7 Reading materials

The first 4 Chapter Decision tree

4.1 The basic flow
4.2 Divide and choose
4.3 Pruning
4.4 Continuous and missing values
4.5 Multivariate decision trees
4.6 Reading materials

The first 5 Chapter neural network

5.1 Neuron model
5.2 Perceptrons and multilayer networks
5.3 Error back propagation algorithm
5.4 Global minimum and local minimum
5.5 Other common neural networks
5.6 Deep learning
5.7 Reading materials

The first 6 Chapter Support vector machine

6.1 Interval and support vector
6.2 The dual problem
6.3 Kernel function
6.4 Soft interval and regularization
6.5 Support vector regression
6.6 Nuclear method
6.7 Reading materials

The first 7 Chapter Bayesian classifier

7.1 Bayesian decision theory
7.2 Maximum likelihood estimation
7.3 Naive Bayes classifier
7.4 Semi naive Bayesian classifier
7.5 Bayesian networks
7.6 EM Algorithm
7.7 Reading materials

The first 8 Chapter Integrated learning

8.1 Individual and integration
8.2 Boosting
8.3 Bagging And random forests
8.4 Combination strategy
8.5 diversity
8.6 Reading materials

The first 9 Chapter clustering

9.1 Clustering tasks
9.2 Performance metrics
9.3 Distance calculation
9.4 Prototype clustering
9.5 Density clustering
9.6 Hierarchical clustering
9.7 Reading materials

The first 10 Chapter Dimension reduction and measurement learning

10.1 K Proximity learning
10.2 Low dimensional embedding
10.3 Principal component analysis
10.4 Kernel linear dimensionality reduction
10.5 Manifold learning
10.6 Measure learning
10.7 Reading materials

The first 11 Chapter Feature selection and sparse learning

11.1 Subset search and evaluation
11.2 Filtering is choice
11.3 Package selection
11.4 Embedded selection and L1 Regularization
11.5 Sparse representation and dictionary learning
11.6 Compress perception
11.7 Reading materials

The first 12 Chapter Computational learning theory

12.1 Basic knowledge of
12.2 PAC Study
12.3 Finite hypothetical space
12.4 VC dimension
12.5 Rademacher Complexity
12.6 stability
12.7 Reading materials

The first 13 Chapter Semi-supervised learning

13.1 Semi labeled sample
13.2 Generative approach
13.3 Semi supervision SVM
13.4 Figure half supervised learning
13.5 The divergence based approach
13.6 Semi supervised clustering
13.7 Reading materials

The first 14 Chapter Probability graph model

14.1 hidden Markov model
14.2 Markov random Airport
14.3 Conditional random field
14.4 Learning and inference
14.5 Approximate inference
14.6 Topic model
14.7 Reading materials

The first 15 Chapter Rule learning

15.1 Basic concepts
15.2 Sequential coverage
15.3 Pruning optimization
15.4 First order rule learning
15.5 Inductive logic programming
15.6 Reading materials

The first 16 Chapter Reinforcement learning

16.1 Tasks and rewards
16.2 K- Swing arm gambling machine
16.3 There is model learning
16.4 Model free learning
16.5 Value function approximation
16.6 Imitation learning
16.7 Reading materials

appendix
A matrix
B Optimize
C A probability distribution