Machine Learning: A Probabilistic Perspective

Today’s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package–PMTK (probabilistic modeling toolkit)–that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Table of Contents

1 Introduction
2 Probability
3 Generative Models for Discrete Data
4 Gaussian Models
5 Bayesian Statistics
6 Frequentist Statistics
7 Linear Regression
8 Logistic Regression
9 Generalized Linear Models and the Exponential Family
10 Directed Graphical Models (Bayes Nets)
11 Mixture Models and the EM Algorithm
12 Latent Linear Models
13 Sparse Linear Models
14 Kernels
15 Gaussian Processes
16 Adaptive Basis Function Models
17 Markov and Hidden Markov Models
18 State Space Models
19 Undirected Graphical Models (Markov Random Fields)
20 Exact Inference for Graphical Models
21 Variational Inference
22 More Variational Inference
23 Monte Carlo Inference
24 Markov Chain Monte Carlo (MCMC) Inference
25 Clustering
26 Graphical Model Structure Learning
27 Latent Variable Models for Discrete Data
28 Deep Learning