Table of Contents

Preface		xiii
Chapter 1	Introduction	1
	1.1 Machine learning vs. statistics	4
	1.2 This book and the ancillary material	5
	1.3 Types of machine learning models	6
	1.4 Generative AI	8
	1.5 Validation and testing	10
	1.6 Data cleaning	18
	1.7 Bayes theorem	20
	Summary	22
	Short concept questions	24
	Exercises	24
Chapter 2	Unsupervised Learning	27
	2.1 Feature scaling	28
	2.2 The k-means algorithm	29
	2.3 Choosing k	34
	2.4 The curse of dimensionality	37
	2.5 Country risk	38
	2.6 Alternative clustering approaches	43
	2.7 Principal components analysis	45
	Summary	49
	Short concept questions	50
	Exercises	51
Chapter 3	Supervised Learning: Linear and Logistic Regression	53
	3.1 Linear regression: one feature	54
	3.2 Linear regression: multiple features	55
	3.3 Categorical features	58
	3.4 Regularization	60
	3.5 Ridge regression	60
	3.6 Lasso regression	64
	3.7 Elastic Net regression	67
	3.8 Application to Predicting House Prices	68
	3.9 Logistic regression	73
	3.10 Decision criteria	75
	3.11 Application to credit decisions	76
	3.12 The k-nearest neighbor algorithm	83
	Summary	84
	Short concept questions	85
	Exercises	86
Chapter 4	Supervised Learning: Decision Trees	89
	4.1 Nature of decision trees	90
	4.2 Information gain measures	91
	4.3 Application to LendingClub data	93
	4.4 The nave Bayes classifier	98
	4.5 Continuous target variables	102
	4.6 Ensemble learning	106
	Summary	107
	Short concept questions	108
	Exercises	109
Chapter 5	Supervised Learning: SVMs	111
	5.1 Linear SVM classification	111
	5.2 Modification for soft margin	118
	5.3 Non-linear separation	121
	5.4 Predicting a targets value	123
	Summary	126
	Short concept questions	127
	Exercises	128
Chapter 6	Supervised Learning: Neural Networks	129
	6.1 ANNs with a single hidden layer	129
	6.2 Multi-layer ANNs	134
	6.3 Other activation functions	136
	6.4 Gradient descent algorithm	138
	6.5 Variations on the basic method	144
	6.6 The stopping rule	145
	6.7 The Black−Scholes−Merton formula	147
	6.8 Extensions	150
	6.9 Understanding volatility movements	151
	Summary	156
	Short concept questions	156
	Exercises	157
Chapter 7	Further Applications of Neural Networks	159
	7.1 Autoencoders and PCA	159
	7.2 General autoencoder design	162
	7.3 Variational autoencoders	164
	7.4 Generative adversarial networks	166
	7.5 Recurrent neural networks	169
	7.6 Convolutional neural networks	170
	Summary	174
	Short concept questions	175
	Exercises	176
Chapter 8	Reinforcement Learning	177
	8.1 The multi-armed bandit problem	178
	8.2 Changing environment	183
	8.3 The game of Nim	185
	8.4 Temporal difference learning	188
	8.5 When the opponent learns	191
	8.6 Deep Q-learning	192
	8.7 Distributional reinforcement learning	192
	8.8 Playing chess	193
	8.9 Applications	194
	8.10 Optimal trade execution	196
	8.11 Data issues	198
	Summary	199
	Short concept questions	200
	Exercises	201
Chapter 9	Natural Language Processing	203
	9.1 NLP Generations	204
	9.2 Generation 1	206
	9.3 Generation 2	208
	9.4 Generation 3	210
	9.5 Generation 4	212
	Summary	212
	Short concept questions	213
	Exercises	214
Chapter 10	Sentiment Analysis	215
	10.1 Pre-processing	217
	10.2 Word Lists	218
	10.3 Naive Bayes classifier	219
	10.4 Application of other algorithms	223
	10.5 Sentiment analysis for stock trading	224
	Summary	227
	Short concept questions	227
	Exercises	227
Chapter 11	Large Language Models	229
	11.1 General Concepts of LLMs	230
	11.2 Foundation LLMs and limitations	232
	11.3 Retrieval augmented generation	235
	11.4 Prompt Engineering	237
	11.5 Applications of LLMs	239
	11.6 LLM agentic system	245
	Summary	246
	Short concept questions	246
	Exercises	247
Chapter 12	Model Interpretability	249
	12.1 Linear regression	252
	12.2 Logistic regression	255
	12.3 Black-box models	258
	12.4 Local interpretability	260
	12.5 Global Interpretability	264
	Summary	265
	Short concept questions	266
	Exercises	267
Chapter 13	Issues for Society and AI Regulations	269
	13.1 Data privacy	270
	13.2 Biases	271
	13.3 Ethics	273
	13.4 Transparency	274
	13.5 Adversarial machine learning	275
	13.6 Legal issues	276
	13.7 AI Regulations	277
	13.8 Man vs. machine	279
Answers to Short Concept Questions			283
Glossary of Terms			305
Index			319