Joeyonng
Notebook
Pages
About
Backyard
Machine Learning
41
Support Vector Machine
Welcome
Notations and Facts
Linear Algebra
1
Fields and Spaces
2
Vectors and Matrices
3
Span and Linear Independence
4
Basis and Dimension
5
Linear Map and Rank
6
Inner Product and Norm
7
Orthogonality and Orthogonal Matrix
8
Complementary Subspaces and Projection
9
Orthogonal Complement and Decomposition
10
SVD and Pseudoinverse
11
Orthogonal and Affine Projection
12
Determinants and Eigensystems
13
Similarity and Diagonalization
14
Normal and Positive Definite Matrices
Calculus
15
Derivatives
16
Chain rule
Probability and Statistics
17
Probability
18
Random Variables
19
Expectation
20
Common Distributions
21
Moment Generating Function
22
Concentration Inequalities I
23
Convergence
24
Limit Theorems
25
Maximum Likelihood Estimation
26
Bayesian Estimation
27
Expectation-maximization
28
Concentration Inequalities II
Learning Theory
29
Statistical Learning
30
Bayesian Classifier
31
Effective Class Size
32
Empirical Risk Minimization
33
Uniform Convergence
34
PAC Learning
35
Rademacher Complexity
Machine Learning
36
Linear Discriminant
37
Perceptron
38
Logistic Regression
39
Multi-layer Perceptron
40
Boosting
41
Support Vector Machine
42
Decision Tree
41
Support Vector Machine
TODO
40
Boosting
42
Decision Tree