Tutorial Slides by Andrew Moore
http://www.autonlab.org/tutorials/list.html
The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.
Reading online notes and doing problems from other professors' course webpages is the best way to learn ML! Here is a collection of links from schools such as CMU,Berkeley,MIT,Stanford,Brown,etc They are roughly sorted by some arcane criterion which roughly corresponds to how useful I found them to be.On the bottom of this page you can find some links related to vision and learning. These aren't your everyday computer vision links, only learning based vision!
Note: This course is offered by Stanford as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development.
Covered topics include: decision theory, maximum likelihood estimation, Bayesian statistics, linear classifiers, support vector machines, nearest neighbor classification, Parzen windows, linear regression, regularization theory, neural networks, boosting, model selection, statistical learning theory, feature selection, graphical models, and various techniques for unsupervised learning.
CPS 271 Machine Learning Duke University
http://www.cs.duke.edu/courses/fall11/cps271/schedule.shtml
Theoretical and practical issues in modern machine learning techniques. Topics include statistical foundations, supervised and unsupervised learning, decision trees, hidden Markov models, neural networks, and reinforcement learning. Minimal overlap with Computer Science 270.
CSE 455/555 Introduction to Pattern Recognition University at Buffalo
http://www.cse.buffalo.edu/~jcorso/t/2011S_555/
Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems
Stat 231--- CS 276A Pattern Recognition and Machine Learning UCLA
http://www.stat.ucla.edu/~sczhu/Courses/UCLA/Stat_231/Stat_231.html
This course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning, which are used in computer vision, speech recognition, data mining, statistics, information retrieval, and bioinformatics. Topics include: Bayesian decision theory, parametric and non-parametric learning, data clustering, component analysis, boosting techniques, kernel methods and support vector machine, and fast nearest neighbor indexing and hashing.
CSE 802 - Pattern Recognition and Analysis Michigan State University
http://www.cse.msu.edu/~cse802/
G22-2565-001, Fall 2010: Machine Learning and Pattern Recognition NYU
http://www.cs.nyu.edu/~yann/2010f-G22-2565-001/index.html
http://www.autonlab.org/tutorials/list.html
The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.
Web Resources for Machine Learning and Vision
http://www.cs.cmu.edu/~tmalisie/mllinks.htmlReading online notes and doing problems from other professors' course webpages is the best way to learn ML! Here is a collection of links from schools such as CMU,Berkeley,MIT,Stanford,Brown,etc They are roughly sorted by some arcane criterion which roughly corresponds to how useful I found them to be.On the bottom of this page you can find some links related to vision and learning. These aren't your everyday computer vision links, only learning based vision!
Machine Learning Andrew Ng, Stanford
http://academicearth.org/courses/machine-learningNote: This course is offered by Stanford as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development.
Machine Learning & Pattern Recognition (Brown University)
http://www.cs.brown.edu/courses/cs295-3/ Covered topics include: decision theory, maximum likelihood estimation, Bayesian statistics, linear classifiers, support vector machines, nearest neighbor classification, Parzen windows, linear regression, regularization theory, neural networks, boosting, model selection, statistical learning theory, feature selection, graphical models, and various techniques for unsupervised learning.
CPS 271 Machine Learning Duke University
http://www.cs.duke.edu/courses/fall11/cps271/schedule.shtml
Theoretical and practical issues in modern machine learning techniques. Topics include statistical foundations, supervised and unsupervised learning, decision trees, hidden Markov models, neural networks, and reinforcement learning. Minimal overlap with Computer Science 270.
CSE 455/555 Introduction to Pattern Recognition University at Buffalo
http://www.cse.buffalo.edu/~jcorso/t/2011S_555/
Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems
Stat 231--- CS 276A Pattern Recognition and Machine Learning UCLA
http://www.stat.ucla.edu/~sczhu/Courses/UCLA/Stat_231/Stat_231.html
This course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning, which are used in computer vision, speech recognition, data mining, statistics, information retrieval, and bioinformatics. Topics include: Bayesian decision theory, parametric and non-parametric learning, data clustering, component analysis, boosting techniques, kernel methods and support vector machine, and fast nearest neighbor indexing and hashing.
CSE 802 - Pattern Recognition and Analysis Michigan State University
http://www.cse.msu.edu/~cse802/
G22-2565-001, Fall 2010: Machine Learning and Pattern Recognition NYU
http://www.cs.nyu.edu/~yann/2010f-G22-2565-001/index.html
