ML4ENG — Osvaldo Simeone

TextbookMachine Learning for Engineers

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes an accessible overview of the relationships between machine learning and signal processing, clear explanations of state-of-the-art techniques vs. classical methods, demonstration of links between information-theoretic concepts and practical engineering relevance, and reproducible examples using Matlab.

Publisher's webpage · Slides for short course · Full slides

Part I: Introduction and Background

1. When and how to use machine learning
2. Background

Part II: Fundamental Concepts and Algorithms

3. Inference, or model-driven prediction
4. Supervised learning: getting started
5. Optimization for machine learning
6. Supervised learning: beyond least squares
7. Unsupervised learning
8. Statistical learning theory
9. Exponential family of distributions
10. Variational inference and variational expectation maximization
11. Information-theoretic inference and learning
12. Bayesian learning
13. Transfer learning, multi-task learning, continual learning, and meta-learning
14. Federated learning

Part V: Epilogue

15. Beyond this book

Cite as: O. Simeone, "Machine Learning for Engineers," Cambridge University Press, 2022. For feedback, please contact me directly.

TextbookMachine Learning for Engineers

Table of Contents