Introduction to Machine Learning
Class notes of Machine Learning course given at the Hebrew University of Jerusalem.
Tag(s): Machine Learning
Publication date: 23 Apr 2009
ISBN-10: n/a
ISBN-13: n/a
Paperback: 109 pages
Views: 9,098
Type: N/A
Publisher: n/a
License: n/a
Post time: 30 May 2016 12:00:00
Introduction to Machine Learning
Class notes of Machine Learning course given at the Hebrew University of Jerusalem.
Publication date: 23 Apr 2009
ISBN-10: n/a
ISBN-13: n/a
Paperback: 109 pages
Views: 9,098
Document Type: N/A
Publisher: n/a
License: n/a
Post time: 30 May 2016 12:00:00
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).
More information is available at arxiv.org.
Table of Contents:
Bayesian Decision Theory - Maximum Likelihood/ Maximum Entropy Duality - EM Algorithm: ML over Mixture of Distributions - Support Vector Machines and Kernel Functions - Spectral Analysis I: PCA, LDA, CCA - Spectral Analysis II: Clustering - The Formal (PAC) Learning Model - The VC Dimension - The Double-Sampling Theorem
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About The Author(s)
Amnon Shashua is a computer science professor at the Hebrew University in Jerusalem as well as Co-founder and CTO of Mobileye and Co-founder of Orcam. His work includes early visual processing of Saliency and Grouping mechanisms, Visual Recognition and Learning, Image Synthesis for Animation and Graphics, theory of Computer Vision in the areas of multiple-view geometry and multi-view tensors, multilinear algebraic systems in Vision and Learning and primal/dual optimization for approximate inference in MRF and Graphical models and recently on deep layered networks.
Amnon Shashua is a computer science professor at the Hebrew University in Jerusalem as well as Co-founder and CTO of Mobileye and Co-founder of Orcam. His work includes early visual processing of Saliency and Grouping mechanisms, Visual Recognition and Learning, Image Synthesis for Animation and Graphics, theory of Computer Vision in the areas of multiple-view geometry and multi-view tensors, multilinear algebraic systems in Vision and Learning and primal/dual optimization for approximate inference in MRF and Graphical models and recently on deep layered networks.