Python：Advanced Guide to Artificial Intelligence

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Title Page

Copyright and Credits

Python: Advanced Guide to Artificial Intelligence

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Preface

Who this book is for

What this book covers

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Conventions used

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Machine Learning Model Fundamentals

Models and data

Zero-centering and whitening

Training and validation sets

Cross-validation

Features of a machine learning model

Capacity of a model

Vapnik-Chervonenkis capacity

Bias of an estimator

Underfitting

Variance of an estimator

Overfitting

The Cramér-Rao bound

Loss and cost functions

Examples of cost functions

Mean squared error

Huber cost function

Hinge cost function

Categorical cross-entropy

Regularization

Ridge

Lasso

ElasticNet

Early stopping

Summary

Introduction to Semi-Supervised Learning

Semi-supervised scenario

Transductive learning

Inductive learning

Semi-supervised assumptions

Smoothness assumption

Cluster assumption

Manifold assumption

Generative Gaussian mixtures

Example of a generative Gaussian mixture

Weighted log-likelihood

Contrastive pessimistic likelihood estimation

Example of contrastive pessimistic likelihood estimation

Semi-supervised Support Vector Machines (S3VM)

Example of S3VM

Transductive Support Vector Machines (TSVM)

Example of TSVM

Summary

Graph-Based Semi-Supervised Learning

Label propagation

Example of label propagation

Label propagation in Scikit-Learn

Label spreading

Example of label spreading

Label propagation based on Markov random walks

Example of label propagation based on Markov random walks

Manifold learning

Isomap

Example of Isomap

Locally linear embedding

Example of locally linear embedding

Laplacian Spectral Embedding

Example of Laplacian Spectral Embedding

t-SNE

Example of t-distributed stochastic neighbor embedding

Summary

Bayesian Networks and Hidden Markov Models

Conditional probabilities and Bayes' theorem

Bayesian networks

Sampling from a Bayesian network

Direct sampling

Example of direct sampling

A gentle introduction to Markov chains

Gibbs sampling

Metropolis-Hastings sampling

Example of Metropolis-Hastings sampling

Sampling example using PyMC3

Hidden Markov Models (HMMs)

Forward-backward algorithm

Forward phase

Backward phase

HMM parameter estimation

Example of HMM training with hmmlearn

Viterbi algorithm

Finding the most likely hidden state sequence with hmmlearn

Summary

EM Algorithm and Applications