Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)

Contents
Series Foreword
Preface
1: Why We Are Interested in Machine Learning
	The Power of the Digital
	Computers Store Data
	Computers Exchange Data
	Mobile Computing
	Social Data
	All That Data: The Dataquake
	Learning versus Programming
	Artificial Intelligence
	Understanding the Brain
	Pattern Recognition
	What We Talk about When We Talk about Learning
	History
2: Machine Learning, Statistics, and Data Analytics
	Learning to Estimate the Price of a Used Car
	Randomness and Probability
	Learning a General Model
	Model Selection
	Supervised Learning
	Learning a Sequence
	Credit Scoring
	Expert Systems
	Expected Values
3: Pattern Recognition
	Learning to Read
	Matching Model Granularity
	Generative Models
	Face Recognition
	Speech Recognition
	Natural Language Processing and Translation
	Combining Multiple Models
	Outlier Detection
	Dimensionality Reduction
	Decision Trees
	Active Learning
	Learning to Rank
	Bayesian Methods
4: Neural Networks and Deep Learning
	Artificial Neural Networks
	Neural Network Learning Algorithms
	What a Perceptron Can and Cannot Do
	Recurrent Networks for Learning Time
	Connectionist Models in Cognitive Science
	Neural Networks as a Paradigm for Parallel Processing
	Hierarchical Representations in Multiple Layers
	Deep Learning
	Learning Hidden Representations
	End-­to-­End Learning
	Generative Adversarial Networks
5: Learning Clusters and Recommendations
	Finding Groups in Data
	Recommendation Systems
6: Learning to Take Action
	Reinforcement Learning
	K-­Armed Bandit
	Temporal Difference Learning
	Learning to Play Games
	Reinforcement Learning in Real Life
7: Challenges and Risks
	The Other Side of Machine Learning
	Data Privacy and Security
	Biased Data
	Model Interpretability
	Ethical, Legal, and Other Social Aspects
8: Where Do We Go from Here?
	Make Them Smart, Make Them Learn
	High-­Performance Computation
	How Green Is My AI?
	Data Mining
	Data Science
	Machine Learning, Artificial Intelligence, and the Future
	Closing Remarks
Glossary
Notes
	Preface
	Chapter 1
	Chapter 2
	Chapter 3
	Chapter 4
	Chapter 7
	Chapter 8
References
Further Reading
Index