Machine Learning Algorithms in Depth (Final Release)

brief contents
contents
preface
acknowledgments
about this book
	Who should read this book
	How this book is organized
	About the code
	liveBook discussion forum
about the author
about the cover illustration
Part 1—Introducing ML algorithms
	1 Machine learning algorithms
		1.1 Types of ML algorithms
		1.2 Why learn algorithms from scratch?
		1.3 Mathematical background
		1.4 Bayesian inference and deep learning
			1.4.1 Two main camps of Bayesian inference: MCMC and VI
			1.4.2 Modern deep learning algorithms
		1.5 Implementing algorithms
			1.5.1 Data structures
			1.5.2 Problem-solving paradigms
	2 Markov chain Monte Carlo
		2.1 Introduction to Markov chain Monte Carlo
			2.1.1 Posterior distribution of coin flips
			2.1.2 Markov chain for page rank
		2.2 Estimating pi
		2.3 Binomial tree model
		2.4 Self-avoiding random walk
		2.5 Gibbs sampling
		2.6 Metropolis-Hastings sampling
		2.7 Importance sampling
		2.8 Exercises
	3 Variational inference
		3.1 KL variational inference
		3.2 Mean-field approximation
		3.3 Image denoising in an Ising model
		3.4 MI maximization
		3.5 Exercises
	4 Software implementation
		4.1 Data structures
			4.1.1 Linear
			4.1.2 Nonlinear
			4.1.3 Probabilistic
		4.2 Problem-solving paradigms
			4.2.1 Complete search
			4.2.2 Greedy
			4.2.3 Divide and conquer
			4.2.4 Dynamic programming
		4.3 ML research: Sampling methods and variational inference
		4.4 Exercises
Part 2—Super vised learning
	5 Classification algorithms
		5.1 Introduction to classification
		5.2 Perceptron
		5.3 Support vector machine
		5.4 Logistic regression
		5.5 Naive Bayes
		5.6 Decision tree (CART)
		5.7 Exercises
	6 Regression algorithms
		6.1 Introduction to regression
		6.2 Bayesian linear regression
		6.3 Hierarchical Bayesian regression
		6.4 KNN regression
		6.5 Gaussian process regression
		6.6 Exercises
	7 Selected super vised learning algorithms
		7.1 Markov models
			7.1.1 Page rank algorithm
			7.1.2 Hidden Markov models
		7.2 Imbalanced learning
			7.2.1 Undersampling strategies
			7.2.2 Oversampling strategies
		7.3 Active learning
			7.3.1 Query strategies
		7.4 Model selection: Hyperparameter tuning
			7.4.1 Bayesian optimization
		7.5 Ensemble methods
			7.5.1 Bagging
			7.5.2 Boosting
			7.5.3 Stacking
		7.6 ML research: Supervised learning algorithms
		7.7 Exercises
Part 3—Unsuper vised learning
	8 Fundamental unsuper vised learning algorithms
		8.1 Dirichlet process K-means
		8.2 Gaussian mixture models
			8.2.1 Expectation maximization (EM) algorithm
		8.3 Dimensionality reduction
			8.3.1 Principal component analysis
			8.3.2 t-SNE manifold learning on images
		8.4 Exercises
	9 Selected unsuper vised learning algorithms
		9.1 Latent Dirichlet allocation
			9.1.1 Variational Bayes
		9.2 Density estimators
			9.2.1 Kernel density estimator
			9.2.2 Tangent portfolio optimization
		9.3 Structure learning
			9.3.1 Chow-Liu algorithm
			9.3.2 Inverse covariance estimation
		9.4 Simulated annealing
		9.5 Genetic algorithm
		9.6 ML research: Unsupervised learning
		9.7 Exercises
Part 4—Deep learning
	10 Fundamental deep learning algorithms
		10.1 Multilayer perceptron
		10.2 Convolutional neural nets
			10.2.1 LeNet on MNIST
			10.2.2 ResNet image search
		10.3 Recurrent neural nets
			10.3.1 LSTM sequence classification
			10.3.2 Multi-input model
		10.4 Neural network optimizers
		10.5 Exercises
	11 Advanced deep learning algorithms
		11.1 Autoencoders
			11.1.1 VAE anomaly detection in time series
		11.2 Amortized variational inference
			11.2.1 Mixture density networks
		11.3 Attention and transformers
		11.4 Graph neural networks
		11.5 ML research: Deep learning
		11.6 Exercises
appendix A—Further reading and resources
	A.1 Competitive programming
	A.2 Recommended books
	A.3 Research conferences
		A.3.1 Machine learning
		A.3.2 Computer vision
		A.3.3 Natural language processing
		A.3.4 Theoretical computer science
appendix B—Answers to exercises
index
	Numerics
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W