فهرست مطالب

• Brief Contents
• Contents
• Preface
• Acknowledgments
• About this Book
• 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
• 1.5 Implementing algorithms
• 2. Markov Chain Monte Carlo
• 2.1 Introduction to Markov chain Monte Carlo
• 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
• 4.2 Problem-solving paradigms
• 4.3 ML research: Sampling methods and variational inference
• 4.4 Exercises
• Part 2—Supervised 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 Supervised Learning Algorithms
• 7.1 Markov models
• 7.2 Imbalanced learning
• 7.3 Active learning
• 7.4 Model selection: Hyperparameter tuning
• 7.5 Ensemble methods
• 7.6 ML research: Supervised learning algorithms
• 7.7 Exercises
• Part 3—Unsupervised Learning
• 8. Fundamental Unsupervised Learning Algorithms
• 8.1 Dirichlet process K-means
• 8.2 Gaussian mixture models
• 8.3 Dimensionality reduction
• 8.4 Exercises
• 9. Selected Unsupervised Learning Algorithms
• 9.1 Latent Dirichlet allocation
• 9.2 Density estimators
• 9.3 Structure learning
• 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
• 10.3 Recurrent neural nets
• 10.4 Neural network optimizers
• 10.5 Exercises
• 11. Advanced Deep Learning Algorithms
• 11.1 Autoencoders
• 11.2 Amortized variational inference
• 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
• Appendix B—Answers to Exercises
• Index