Introduction to Machine Learning, Fourth Edition (Hardcover, 4)

1 Introduction
2 Supervised Learning
3 Bayesian Decision Theory
4 Parametric Methods
5 Multivariate Methods
6 Dimensionality Reduction
7 Clustering
8 Nonparametric Methods
9 Decision Trees
10 Linear Discrimination
11 Multilayer Perceptrons
12 Deep Learning
13 Local Models
14 Kernel Machines
15 Graphical Models
16 Hidden Markov Models
17 Bayesian Estimation
18 Combining Multiple Learners
19 Reinforcement Learning
20 Design and Analysis of Machine Learning Experiments
A Probability
B Linear Algebra
C Optimization