**Foundations of Machine Learning**

by M. Mohri, A. Rostamizadeh, A. Talwalkar

**Publisher**: The MIT Press 2018**ISBN-13**: 9780262039406**Number of pages**: 504

**Description**:

This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.

Download or read it online for free here:

**Read online**

(online reading)

## Similar books

**An Introductory Study on Time Series Modeling and Forecasting**

by

**Ratnadip Adhikari, R. K. Agrawal**-

**arXiv**

This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.

(

**8832**views)

**Introduction To Machine Learning**

by

**Nils J Nilsson**

This book concentrates on the important ideas in machine learning, to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The author surveys the important topics in machine learning circa 1996.

(

**25429**views)

**An Introduction to Probabilistic Programming**

by

**Jan-Willem van de Meent, et al.**-

**arXiv.org**

This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.

(

**2926**views)

**Machine Learning: A Probabilistic Perspective**

by

**Kevin Patrick Murphy**-

**The MIT Press**

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

(

**250**views)