Machine Learning and Data Mining Lecture Notes

Machine Learning and Data Mining Lecture Notes

Lecture notes for CSC 411 Machine Learning and Data Mining course at the University of Toronto.

Publication date: 06 Feb 2012

ISBN-10: n/a

ISBN-13: n/a

Paperback: 134 pages

Views: 3,152

Type: Lecture Notes

Publisher: n/a

License: n/a

Post time: 05 Aug 2016 07:01:33

Machine Learning and Data Mining Lecture Notes

Machine Learning and Data Mining Lecture Notes Lecture notes for CSC 411 Machine Learning and Data Mining course at the University of Toronto.
Tag(s): Data Mining Machine Learning
Publication date: 06 Feb 2012
ISBN-10: n/a
ISBN-13: n/a
Paperback: 134 pages
Views: 3,152
Document Type: Lecture Notes
Publisher: n/a
License: n/a
Post time: 05 Aug 2016 07:01:33
Note:

These are lecture notes used in CSC 411: Machine Learning and Data Mining course for undergraduate at the University of Toronto.

Contents:

Introduction to Machine Learning - Linear Regression - Nonlinear Regression - Quadratics - Basic Probability Theory - Probability Density Functions (PDFs) - Estimation - Classification - Gradient Descent - Cross Validation - Bayesian Methods - Monte Carlo Methods - Principal Components Analysis - Lagrange Multipliers - Clustering - Hidden Markov Models - Support Vector Machines - AdaBoost.




About The Author(s)


David J. Fleet is Professor of Computer Science at the University of Toronto, and Chair of Computer and Mathematical Sciences at the University of Toronto Scarborough. His research interests include aspects of computer vision, image processing, visual perception and visual neuroscience. Most of his specific research has focused on mathematical foundations and algorithms for visual motion analysis, tracking, human pose and motion estimation, and models of motion perception and stereopsis. 
 

David J. Fleet

David J. Fleet is Professor of Computer Science at the University of Toronto, and Chair of Computer and Mathematical Sciences at the University of Toronto Scarborough. His research interests include aspects of computer vision, image processing, visual perception and visual neuroscience. Most of his specific research has focused on mathematical foundations and algorithms for visual motion analysis, tracking, human pose and motion estimation, and models of motion perception and stereopsis. 
 


Aaron Hertzmann is Senior Research Scientist at Adobe Research, San Francisco. He is interested in all areas of computer graphics and computer vision. 

Aaron Hertzmann

Aaron Hertzmann is Senior Research Scientist at Adobe Research, San Francisco. He is interested in all areas of computer graphics and computer vision. 


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