Logo

A Brief Introduction to Machine Learning for Engineers

Large book cover: A Brief Introduction to Machine Learning for Engineers

A Brief Introduction to Machine Learning for Engineers
by

Publisher: arXiv.org
Number of pages: 237

Description:
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems. It introduces fundamental concepts and algorithms by building on first principles, while also exposing the reader to more advanced topics with extensive pointers to the literature, within a unified notation and mathematical framework.

Home page url

Download or read it online for free here:
Download link
(2.1MB, PDF)

Similar books

Book cover: Machine Learning for DesignersMachine Learning for Designers
by - O'Reilly Media
This book introduces you to contemporary machine learning systems and helps you integrate machine-learning capabilities into your user-facing designs. Patrick Hebron explains how machine-learning applications can affect the way you design websites.
(6721 views)
Book cover: Introduction to Machine LearningIntroduction to Machine Learning
by - arXiv
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
(22077 views)
Book cover: Practical Artificial Intelligence Programming in JavaPractical Artificial Intelligence Programming in Java
by - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
(24544 views)
Book cover: A Course in Machine LearningA Course in Machine Learning
by - ciml.info
Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.
(21489 views)