Introduction to Machine Learning

by Etienne Bernard

Overview

Machine learning-a computer's ability to learn-is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better decisions. Whatever your industry or hobby, chances are that these modern artificial intelligence methods will be useful to you as well. Introduction to Machine Learning weaves reproducible coding examples into explanatory text to show what machine learning is, how it can be applied, and how it works. Perfect for anyone new to the world of AI or those looking to further their understanding, the text begins with a brief introduction to the Wolfram Language, the programming language used for the examples throughout the book. From there, readers are introduced to key concepts before exploring common methods and paradigms such as classification, regression, clustering, and deep learning. The math content is kept to a minimum to focus on what matters-applying the concepts in useful contexts. This book is sure to benefit anyone curious about the fascinating field of machine learning.

Product Details

ISBN-13:	9781579550486
Publisher:	Wolfram Media, Incorporated
Publication date:	12/20/2021
Pages:	424
Sales rank:	534,240
Product dimensions:	7.00(w) x 10.00(h) x 0.86(d)

About the Author

Etienne Bernard is a scientist and entrepreneur in the field of machine learning. His goal is to simplify the practice of machine learning in order to spread its usage. Etienne holds a PhD in statistical physics and was the head of the machine learning group at Wolfram Research for seven years. At Wolfram, he led the development of machine learning tools and applications for the Wolfram Language and Wolfram|Alpha. In 2021, Etienne founded NuMind, a startup providing user-friendly machine learning solutions for companies.

Table of Contents

Preface vii

Short Introduction to the Wolfram Language xi

1 What Is Machine Learning? 1

2 Machine Learning Paradigms 9

Supervised Learning 9

Unsupervised Learning 12

Reinforcement Learning 15

Other Learning Paradigms 17

3 Classification 27

Car vs. Truck 27

Titanic Survival 30

Topic Classification 35

Image Identification 40

Classification Measures 45

From Probabilities to Decisions 52

4 Regression 61

Car Stopping Distances 61

Brain Weights 65

Boston Homes 68

Regression Measures 73

5 How It Works 81

Model 81

Nonparametric Methods 83

Parametric Methods 89

Model Generalization 95

Overfitting and Underfitting 97

Regularization 103

Hyperparameter Optimization 106

Why Predictions Are Not Perfect 109

6 Clustering 123

Fisher's Irises 123

Face Clustering 127

News Aggregator 130

DNA Hierarchical Clustering 133

7 Dimensionality Reduction 139

Manifold Learning 139

Data Visualization 145

Search 149

Anomaly Detection & Denoising 151

Missing Data Synthesis 153

Autoencoder 154

Recommendation 158

8 Distribution Learning 165

Univariate Data 165

Fisher's Irises 167

Missing Data Synthesis 175

Anomaly Detection 178

9 Data Preprocessing 183

Preprocessing Pipeline 183

Numeric Data 184

Categorical Data 188

Image 191

Text 196

10 Classic Supervised Learning Methods 211

Illustrative Examples 211

Linear Regression 213

Logistic Regression 217

Nearest Neighbors 222

Decision Tree 227

Random Forest 231

Gradient Boosted Trees 236

Support-Vector Machine 242

Gaussian Process 247

Markov Model 259

11 Deep Learning Methods 271

From Neurons to Networks 271

How Neural Networks Learn 281

Convolutional Networks 302

Recurrent Networks 324

Transformer Networks 348

12 Bayesian Inference 379

Coin Flip Experiment 379

Bayesian Inference 382

Bayesian Learning for Predictive Modeling 385

Probabilistic Programming 395

Going Further 401

Index 403