Machine Learning [MIT OpenCourseware]

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/

First submitted by Lissa on 14 Apr 2010

Updated by Ravi on 24 Sep 2010

Lecture materials, assignments & related MATLAB files for course by MIT prof Tommi Jaakkola

Rate this link

2806 clicks (last 30 days)

Tags for This LinkHelp

Everyone’s Tags

Descriptions and Ratings (3)

Date	Contributor	Description
24 Sep 2010	Ravi	Please, provide webinar for all the topics, it makes clear understanding to all
7 Jun 2010	MathWorks Classroom Resources Team	Course on machine learning with extensive use of MATLAB in the lectures and homeworks. Taught in Fall 2006 by Prof. Tommi Jaakkola. Topics covered: 1 Introduction, linear classification, perceptron update rule 2 Perceptron convergence, generalization 3 Maximum margin classification 4 Classification errors, regularization, logistic regression 5 Linear regression, estimator bias and variance 6 Active learning (cont.), non-linear predictions, kernals 7 Kernal regression, kernels 8 Support vector machine (SVM) and kernels 9 Model selection 10 Model selection criteria 11 Description length, feature selection 12 Combining classifiers, boosting 13 Boosting, margin, and complexity 14 Margin and generalization, mixture models 15 Mixtures and the expectation maximization (EM) algorithm 16 EM, regularization, clustering 17 Clustering 18 Spectral clustering, Markov models 19 Hidden Markov models (HMMs) 20 HMMs (cont.) 21 Bayesian networks 22 Learning Bayesian networks
14 Apr 2010	Lissa	An introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

24 Sep 2010

Ravi

Please, provide webinar for all the topics, it makes clear understanding to all

7 Jun 2010

MathWorks Classroom Resources Team

Course on machine learning with extensive use of MATLAB in the lectures and homeworks. Taught in Fall 2006 by Prof. Tommi Jaakkola.

Topics covered:

1 Introduction, linear classification, perceptron update rule
2 Perceptron convergence, generalization
3 Maximum margin classification
4 Classification errors, regularization, logistic regression
5 Linear regression, estimator bias and variance
6 Active learning (cont.), non-linear predictions, kernals
7 Kernal regression, kernels
8 Support vector machine (SVM) and kernels
9 Model selection
10 Model selection criteria
11 Description length, feature selection
12 Combining classifiers, boosting
13 Boosting, margin, and complexity
14 Margin and generalization, mixture models
15 Mixtures and the expectation maximization (EM) algorithm
16 EM, regularization, clustering
17 Clustering
18 Spectral clustering, Markov models
19 Hidden Markov models (HMMs)
20 HMMs (cont.)
21 Bayesian networks
22 Learning Bayesian networks

14 Apr 2010

Lissa

An introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Please login to add a description or rating.

Tag Activity

Tag	Applied By	Date/Time
downloadable code	Alexander	21 Sep 2012 at 10:15am
academic	Manish	30 Aug 2012 at 4:35pm
academic	uq_83 Qualsiasi	7 May 2012 at 6:10am
course materials	Thomas De Smet	18 Oct 2011 at 8:25am
statistics and data analysis	xiao zhiyong	22 Aug 2011 at 9:23am
downloadable code	sri sathya	30 Sep 2010 at 1:16am
resource	MathWorks Classroom Resources Team	19 Apr 2010 at 12:02pm
statistics and data analysis	MathWorks Classroom Resources Team	19 Apr 2010 at 12:02pm
language english	MathWorks Classroom Resources Team	19 Apr 2010 at 12:02pm
programming and computer science	MathWorks Classroom Resources Team	19 Apr 2010 at 12:01pm
computer science	MathWorks Classroom Resources Team	19 Apr 2010 at 12:01pm
downloadable code	MathWorks Classroom Resources Team	19 Apr 2010 at 12:01pm
course materials	MathWorks Classroom Resources Team	19 Apr 2010 at 12:01pm
academic	MathWorks Classroom Resources Team	19 Apr 2010 at 12:01pm
academic	Lissa	14 Apr 2010 at 5:24pm
language english	Lissa	14 Apr 2010 at 5:24pm
course materials	Lissa	14 Apr 2010 at 5:24pm
downloadable code	Lissa	14 Apr 2010 at 5:24pm
electrical and computer engineering	Lissa	14 Apr 2010 at 5:24pm
statistics and data analysis	Lissa	14 Apr 2010 at 5:24pm