InfoBooks

Foundations of Machine Learning

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

  • 505 pages
  • 4.6 MB
  • PDF

Rigorous treatment of ML foundations covering PAC learning, Rademacher complexity, boosting, and kernel methods. Ideal for readers with strong mathematical background.

Page 1 / 1
100%

Loading the book...

Found it useful? Share it
Download PDF Verified PDF · Secure download

Keep reading

  • Next in this topic

    Machine Learning - Supervised Techniques

    Sepp Hochreiter · 256 pages

    Comprehensive lecture notes on supervised ML techniques from the creator of LSTM networks, covering regression, classification, and ensemble methods.

    Verified PDF · Secure download
  • Interpretable Machine Learning

    Christoph Molnar · 251 pages

    Practical guide to making ML models explainable, covering SHAP, LIME, partial dependence plots, and feature importance methods.

    Verified PDF · Secure download
  • Introduction to machine learning

    Nils J. Nilsson · 188 pages

    Classic Stanford introduction covering decision trees, neural networks, Bayesian learning, and instance-based methods with clear explanations.

    Verified PDF · Secure download
  • Machine Learning

    Jaydip Sen · 154 pages

    Overview of core ML concepts including supervised, unsupervised, and reinforcement learning with practical examples and algorithm comparisons.

    Verified PDF · Secure download
  • Undergraduate Fundamentals of Machine Learning

    William J. Deuschle · 143 pages

    Undergraduate thesis covering ML fundamentals with implementations in Python, suitable for beginners entering the field.

    Verified PDF · Secure download

Explore more Computer Science

See all 31 Computer Science topics

Help us spread the reading habit!

A good book is even better when it’s shared

Send this page to someone who’d love to read it. Everything is free, no sign-up needed.