A Brief Introduction to Machine Learning for Engineers
by Osvaldo Simeone
Publisher: arXiv.org 2017
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.
Download or read it online for free here:
Download link
(2.1MB, PDF)
Similar books
Modeling Agents with Probabilistic Programsby Owain Evans, et al. - AgentModels.org
This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases. The book assumes basic programming experience.
(7527 views)
Introduction to Machine Learningby Amnon Shashua - 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).
(24709 views)
Lecture Notes in Machine Learningby 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 ...
(10928 views)
Reinforcement Learning: An Introductionby Richard S. Sutton, Andrew G. Barto - The MIT Press
The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.
(30733 views)