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Contents
Front Matter
Introduction
1.
Machine Learning Introduction
2.
Internals of Machine Learning
3.
Design of Learning System
4.
Type of Learning I
5.
Types of Learning II
6.
Probability
7.
Probability Distributions
8.
Linear Algebra
9.
Information Theory
10.
Decision Theory and Bayesian Decision Theory
11.
Classification,Nearest Neighbour Evalutions
12.
Decision Trees
13.
Decision Tree Algorithm ID3
14.
Classification and Regression Trees - I
15.
Classification and Regression Trees - II
16.
Support Vector Machines i
17.
Support Vector Machines-II
18.
Neural Networks – I
19.
Neural Networks – II
20.
Genetic Algorithms - I
21.
Genetic Algorithms - II
22.
Introduction to Clustering
23.
Cluster Analysis and Cluster Validity
24.
Semi Supervised Learning
25.
Dimensionality Reduction - I
26.
Dimensionality Reduction - II
27.
Bayes Learning
28.
Naïve Bayes Classification
29.
Bayesian Belief Networks- I
30.
Bayesian Belief Networks-II
31.
Expectation and Maximization
32.
Markov and Hidden Markov Models
33.
HMM– Baum Welsh and Viterbi Algorithms
34.
HMM – EM Algorithm
35.
Basics of Reinforcement Learning-I
36.
Basics of Reinforcement Learning - II
37.
Q Learning
38.
Temporal Difference Learning
Back Matter
Appendix
Machine Learning
8
Linear Algebra
