Statistical Foundations of Machine Learning

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.

**Tag(s):**
Big Data
Machine Learning
Statistics

**Publication date**: 01 Jan 2016

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
n/a

**Views**: 5,248

Statistical Foundations of Machine Learning

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.

From the Book Description:

We are in the era of big data. The procedure for finding useful patterns in data is known by different names in different communities but more and more, it is grouped under the label of machine learning. 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. This manuscript aims to find a good balance between theory and practice by situating most of the theoretical notions in a real context with the help of practical examples and real datasets. All the examples are implemented in the statistical programming language R.

We are in the era of big data. The procedure for finding useful patterns in data is known by different names in different communities but more and more, it is grouped under the label of machine learning. 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. This manuscript aims to find a good balance between theory and practice by situating most of the theoretical notions in a real context with the help of practical examples and real datasets. All the examples are implemented in the statistical programming language R.

Tweet

About The Author(s)

Dr. Souhaib Ben Taieb is a Lecturer in Business Analytics and Data Science at Monash University in Melbourne, Australia. He received an M.Sc and a Ph.D. in Computer Science (Machine learning) from the Free University of Brussels in Belgium. His main research interests include machine learning, large scale time series modeling and forecasting, and smart grid analytics.

Gianluca Bontempi is a Professor at Département d'Informatique, at Université Libre de Bruxelles. He graduated with honors in Electronic Engineering (Politecnico of Milan, Italy) and obtained his PhD in Applied Sciences (ULB, Brussels, Belgium). His interests cover data mining, machine learning, bioinformatics, time series prediction and simulation.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Microsoft .NET
Rexx
Perl
PHP
Python
R
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors