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Machine Learning Algorithms in Depth
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Machine Learning Algorithms in Depth

by Vadim Smolyakov

Data Science

Book Details

Book Title

Machine Learning Algorithms in Depth

Author

Vadim Smolyakov

Publisher

Manning Publications Co

Publication Date

2024

ISBN

9781633439214

Number of Pages

328

Language

English

Format

PDF

File Size

11.6MB

Subject

machine-learning

Table of Contents

  • 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