**Statistical Foundations of Machine Learning**

by Gianluca Bontempi, Souhaib Ben Taieb

2017**Number of pages**: 269

**Description**:

This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. In particular, we focus on supervised learning problems, where the goal is to model the relation between a set of input variables, and one or more output variables, which are considered to be dependent on the inputs in some manner.

Download or read it online for free here:

**Download link**

(7MB, PDF)

## Similar books

**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.

(

**6033**views)

**A Survey of Statistical Network Models**

by

**A. Goldenberg, A.X. Zheng, S.E. Fienberg, E.M. Airoldi**-

**arXiv**

We begin with the historical development of statistical network modeling and then we introduce some examples in the network literature. Our subsequent discussion focuses on prominent static and dynamic network models and their interconnections.

(

**4397**views)

**The Hundred-Page Machine Learning Book**

by

**Andriy Burkov**

This is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.

(

**2099**views)

**Lecture Notes in Machine Learning**

by

**Zdravko Markov**-

**Central Connecticut State University**

Contents: Introduction; Concept learning; Languages for learning; Version space learning; Induction of Decision trees; Covering strategies; Searching the generalization / specialization graph; Inductive Logic Progrogramming; Unsupervised Learning ...

(

**5884**views)