Introduction to Machine Learning
by Amnon Shashua
Publisher: arXiv 2009
Number of pages: 109
Description:
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).
Download or read it online for free here:
Download link
(680KB, PDF)
Similar books
Machine Learning, Neural and Statistical Classificationby D. Michie, D. J. Spiegelhalter - Ellis Horwood
The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.
(29906 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.
(28945 views)
Statistical Foundations of Machine Learningby Gianluca Bontempi, Souhaib Ben Taieb
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.
(10276 views)
Inductive Logic Programming: Theory and Methodsby Stephen Muggleton, Luc de Raedt - ScienceDirect
Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.
(37950 views)