Artificial Intelligence: A Modern Approach
Artificial Intelligence
Book Details
Book Title
Artificial Intelligence: A Modern Approach
Author
Stuart J. Russell, Peter Norvig
Publisher
Pearson
Publication Date
2021
ISBN
9780134610993, 1292401133, 9781292401133, 9781292401171
Number of Pages
1167
Language
English
Format
File Size
36MB
Subject
Artificial Intelligence
Table of Contents
- Cover
- Information Science and Statistics
- Pattern Recognition and Machine Learning
- ©
- Preface
- Mathematical notation
- Contents
- Chapter 1: Introduction
- 1.1 Example: Polynomial Curve Fitting
- 1.2 Probability Theory
- 1.3 Model Selection
- 1.4 The Curse of Dimensionality
- 1.5 Decision Theory
- 1.6 Information Theory
- Exercises
- Chapter 2: Probability Distributions
- 2.1 Binary Variables
- 2.2 Multinomial Variables
- 2.3 The Gaussian Distribution
- 2.4 The Exponential Family
- 2.5 Nonparametric Methods
- Exercises
- Chapter 3: Linear Models for Regression
- 3.1 Linear Basis Function Models
- 3.2 The Bias-Variance Decomposition
- 3.3 Bayesian Linear Regression
- 3.4 Bayesian Model Comparison
- 3.5 The Evidence Approximation
- 3.6 Limitations of Fixed Basis Functions
- Exercises
- Chapter 4: Linear Models for Classification
- 4.1 Discriminant Functions
- 4.2 Probabilistic Generative Models
- 4.3 Probabilistic Discriminative Models
- 4.4 The Laplace Approximation
- 4.5 Bayesian Logistic Regression
- Exercises
- Chapter 5: Neural Networks
- 5.1 Feed-forward Network Functions
- 5.2 Network Training
- 5.3 Error Backpropagation
- 5.4 The Hessian Matrix
- 5.5 Regularization in Neural Networks
- 5.6 Mixture Density Networks
- 5.7 Bayesian Neural Networks
- Exercises
- Chapter 6: Kernel Methods
- 6.1 Dual Representations
- 6.2 Constructing Kernels
- 6.3 Radial Basis Function Networks
- 6.4 Gaussian Processes
- Exercises
- Chapter 7: Sparse Kernel Machines
- 7.1 Maximum Margin Classifiers
- 7.2 Relevance Vector Machines
- Exercises
- Chapter 8: Graphical Models
- 8.1 Bayesian Networks
- 8.2 Conditional Independence
- 8.3 Markov Random Fields
- 8.4 Inference in Graphical Models
- Exercises
- Chapter 9: Mixture Models and EM
- 9.1 K-means Clustering
- 9.2 Mixtures of Gaussians
- 9.3 An Alternative View of EM
- 9.4 The EM Algorithm in General
- Exercises
- Chapter 10: Approximate Inference
- 10.1 Variational Inference
- 10.2 Illustration: Variational Mixture of Gaussians
- 10.3 Variational Linear Regression
- 10.4 Exponential Family Distributions
- 10.5 Local Variational Methods
- 10.6 Variational Logistic Regression
- 10.7 Expectation Propagation
- Exercises
- Chapter 11: Sampling Methods
- 11.1 Basic Sampling Algorithms
- 11.2 Markov Chain Monte Carlo
- 11.3 Gibbs Sampling
- 11.4 Slice Sampling
- 11.5 The Hybrid Monte Carlo Algorithm
- 11.6 Estimating the Partition Function
- Exercises
- Chapter 12: Continuous Latent Variables
- 12.1 Principal Component Analysis
- 12.2 Probabilistic PCA
- 12.3 Kernel PCA
- 12.4 Nonlinear Latent Variable Models
- Exercises
- Chapter 13: Sequential Data
- 13.1 Markov Models
- 13.2 Hidden Markov Models
- 13.3 Linear Dynamical Systems
- Exercises
- Chapter 14: Combining Models
- 14.1 Bayesian Model Averaging
- 14.2 Committees
- 14.3 Boosting
- 14.4 Tree-based Models
- 14.5 Conditional Mixture Models
- Exercises
- Appendix A. Data Sets
- Appendix B. Probability Distributions
- Appendix C. Properties of Matrices
- Appendix D. Calculus of Variations
- Appendix E. Lagrange Multipliers
- References
- Index