图书简介

1. Introduction; 1.1 The basic idea and history of Machine Learning; 1.2 Mathematical formulation of the basic learning problem; 1.3 Nonlinear regression in highdimensions; 1.4 Recent advancements; 1.5 No free lunch; I A PRACTICAL GUIDE TO MACHINE LEARNING; 2. Scientific programming with Python; 2.1 Programming environment; 2.2 Basic language elements; 2.3 Code efficiency and vectorization; 2.4 Data handling; 2.5 Image processing and convolutional filters; 3. Machine learning with sklearn; 3.1 Classification with SVC, RFC and MLP; 3.2 Performance measures and evaluations; 3.3 Data handling; 3.4 Dimensionality reduction, feature selection, and tSN; 3.5 Decision Trees and Random Forests *; 3.6 Support Vector Machines (SVM) *; 4. Neural Networks and Keras; 4.1 Neurons and the threshold perceptron; 4.2 Multilayer Perceptron (MLP) and Keras; 4.3 Representational learning; 4.4 Convolutional Neural Networks (CNNs); 4.5 What and Where; 4.6 More tricks of the trade; II FOUNDATIONS: REGRESSION AND PROBABILISTIC MODELING; 5. Regression and optimization; 5.1 Linear regression and gradient descent; 5.2 Error surface and challenges for gradient descent; 5.3 Advanced gradient optimization (learning); 5.4 Regularization: Ridge regression and LASSO; 5.5 Nonlinear regression; 5.6 Backpropagation; 5.7 Automatic differentiation; 6. Basic probability theory; 6.1 Random numbers and their probability (density) function; 6.2 Moments: mean, variance, etc.; 6.3 Examples of probability (density) functions; 6.4 Some advanced concepts; 6.5 Density functions of multiple random variables; 6.6 How to combine prior knowledge with new evidence; 7. Probabilistic regression and Bayes nets; 7.1 Probabilistic models; 7.2 Learning in probabilistic models: Maximum likelihood estimate; 7.3 Probabilistic classification; 7.4 MAP and Regularization with priors; 7.5 Bayes Nets: Multivariate causal modeling; 7.6 Probabilistic and Stochastic Neural Networks; 8. Generative Models; 8.1 Modelling classes; 8.2 Supervised generative models; 8.3 Naive Bayes; 8.4 Unsupervised generative models; 8.5 Generative Neural Networks; III ADVANCED LEARNING MODELS; 9. Cyclic Models and Recurrent Neural Networks; 9.1 Sequence processing; 9.2 Simple Sequence MLP and RNN in Keras; 9.3 Gated RNN and attention; 9.4 Models with symmetric lateral connections; 10. Reinforcement Learning; 10.1 Formalization of the problem setting; 10.2 Modelbased Reinforcement Learning; 10.3 Modelfree Reinforcement Learning; 10.4 Deep Reinforcement Learning; 10.5 Actors and actorcritics; 11. AI, the brain, and our society; 11.1 Different levels of modeling and the brain; 11.2 Machine learning and AI; 11.3 The impact machine learning technology on society

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