Statistical Foundations of Machine Learning, Second Edition

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**: 08 Feb 2021

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
376 pages

**Views**: 9,825

Statistical Foundations of Machine Learning, Second Edition

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 Preface:

Gianluca Bontempi wrote:The book is dedicated to all students interested about machine learning which are not satisfied with running some lines of (deep-learning) code but are eager to know about assumptions, limitations, and perspectives of this discipline. When I was a student, my dream was to become an AI researcher and save humankind with intelligent robots. For several reasons, I quit such ambition (but you never know). In exchange, I discovered that machine learning is much more than a conventional research domain since it aims to make automatic the scientific process bringing from observations to knowledge.

The first version of this book was made publicly available in 2004 with two objectives and one ambition. The first objective was to provide a handbook to ULB students since I was (and still am) strongly convinced that a decent course should come with a decent handbook. The second objective was to group all the material that I consider fundamental (or at least essential) for a Ph.D. student to undertake a thesis in my lab. At that time there were already plenty of excellent machine learning reference books. However, most of the existing work concealed (or did not make explicit enough, notably because of incomplete or inconsistent notation) important statistical assumptions underlying the process of inferring models from data.

Tweet

About The Author(s)

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
Rexx
Microsoft .NET
Perl
PHP
R
Python
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors