Probabilistic Machine Learning: An Introduction
Genres: Computers & Technology
Book Preface
A popular definition of machine learning or ML, due to Tom Mitchell [Mit97], is as follows:
A computer program is said to learn from experience E with respect to some class of tasks T, and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Thus there are many different kinds of machine learning, depending on the nature of the task T we wish the system to learn, the nature of the performance measure P we use to evaluate the system, and the nature of the training signal or experience E we give it.
In this book, we will cover the most common types of ML, but from a probabilistic perspective. Roughly speaking, this means that we treat all unknown quantities (e.g., predictions about the future value of some quantity of interest, such as tomorrow’s temperature, or the parameters of some model) as random variables, that are endowed with probability distributions which describe a weighted set of possible values the variable may have. (See Chapter 2 for a quick refresher on the basics of probability, if necessary.)
There are two main reasons we adopt a probabilistic approach. First, it is the optimal approach to decision making under uncertainty, as we explain in Section 5.1. Second, probabilistic modeling is the language used by most other areas of science and engineering, and thus provides a unifying framework between these fields. As Shakir Mohamed, a researcher at DeepMind, put it:1
Almost all of machine learning can be viewed in probabilistic terms, making probabilistic thinking fundamental. It is, of course, not the only view. But it is through this view that we can connect what we do in machine learning to every other computational science, whether that be in stochastic optimisation, control theory, operations research, econometrics, information theory, statistical physics or bio-statistics. For this reason alone, mastery of probabilistic thinking is essential.
Brief Contents
1 Introduction
I Foundations
2 Probability: Univariate Models
3 Probability: Multivariate Models
4 Statistics
5 Decision Theory
6 Information Theory
7 Linear Algebra
8 Optimization
II Linear Models
9 Linear Discriminant Analysis
10 Logistic Regression
11 Linear Regression
12 Generalized Linear Models *
III Deep Neural Networks
13 Neural Networks for Structured Data
14 Neural Networks for Images
15 Neural Networks for Sequences
IV Nonparametric Models
16 Exemplar-based Methods
17 Kernel Methods *
18 Trees, Forests, Bagging, and Boosting
V Beyond Supervised Learning
19 Learning with Fewer Labeled Examples
20 Dimensionality Reduction
21 Clustering
22 Recommender Systems
23 Graph Embeddings *
A Notation
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