**Understanding Machine Learning: From Theory to Algorithms**

by Shai Shalev-Shwartz, Shai Ben-David

**Publisher**: Cambridge University Press 2014**ISBN/ASIN**: 1107057132**ISBN-13**: 9781107057135**Number of pages**: 449

**Description**:

The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

Download or read it online for free here:

**Download link**

(2.5MB, PDF)

## Similar books

**Optimal and Learning Control for Autonomous Robots**

by

**Jonas Buchli, et al.**-

**arXiv.org**

The starting point is the formulation of of an optimal control problem and deriving the different types of solutions and algorithms from there. These lecture notes aim at supporting this unified view with a unified notation wherever possible.

(

**699**views)

**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.

(

**5056**views)

**Machine Learning and Data Mining: Lecture Notes**

by

**Aaron Hertzmann**-

**University of Toronto**

Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.

(

**3724**views)

**Information Theory, Inference, and Learning Algorithms**

by

**David J. C. MacKay**-

**Cambridge University Press**

A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.

(

**14530**views)