Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
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! What's new | ! What's new | ||
|- | |- | ||
− | | 9/2 || ''No class - Labor day'' || | + | | M 9/2 || ''No class - Labor day'' || |
|- | |- | ||
− | | 9/4 || [[10-601 Introduction to Probability|Overview and Intro to Probability]] || William | + | | W 9/4 || [[10-601 Introduction to Probability|Overview and Intro to Probability]] || William |
|- | |- | ||
− | | 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William | + | | M 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William |
|- | |- | ||
− | | 9/11 || [[10-601 Logistic Regression|Logistic Regression]] || William | + | | W 9/11 || [[10-601 Logistic Regression|Logistic Regression]] || William |
|- | |- | ||
− | | 9/16 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William | + | | M 9/16 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William |
|- | |- | ||
− | | 9/18 || Evaluating and comparing classifiers || William | + | | W 9/18 || Evaluating and comparing classifiers || William |
|- | |- | ||
− | | 9/23 || Neural networks || Eric | + | | M 9/23 || Neural networks || Eric |
|- | |- | ||
− | | 9/25 || K-nearest neighbor classifiers || Eric | + | | W 9/25 || K-nearest neighbor classifiers || Eric |
|- | |- | ||
− | | 9/30 || Decision trees || Eric | + | | M 9/30 || Decision trees || Eric |
|- | |- | ||
− | | 10/2 || PAC-learning and learning theory || William | + | | W 10/2 || PAC-learning and learning theory || William |
|- | |- | ||
− | | 10/7 || Bias-variance and linear regression || William | + | | M 10/7 || Bias-variance and linear regression || William |
|- | |- | ||
− | | 10/9 || K-means and Mixture models || Eric | + | | W 10/9 || K-means and Mixture models || Eric |
|- | |- | ||
− | | 10/14 || Dimensionality Reduction || Eric | + | | M 10/14 || Dimensionality Reduction || Eric |
|- | |- | ||
− | | 10/16 || Modeling distributions with Bayes Nets and Markov Fields || Eric | + | | W 10/16 || Modeling distributions with Bayes Nets and Markov Fields || Eric |
|- | |- | ||
− | | 10/21 || Learning with graphical models || Eric | + | | M 10/21 || Learning with graphical models || Eric |
|- | |- | ||
− | | 10/23 || Topic models || William | + | | W 10/23 || Topic models || William |
|- | |- | ||
− | | 10/28 || HMMs || Eric | + | | M 10/28 || HMMs || Eric |
|- | |- | ||
− | | 10/30 | + | | W 10/30 |
|- | |- | ||
− | | 11/4 | + | | M 11/4 |
|- | |- | ||
− | | 11/6 | + | | W 11/6 |
|- | |- | ||
− | | 11/11 | + | | M 11/11 |
|- | |- | ||
− | | 11/13 | + | | W 11/13 |
|- | |- | ||
− | | 11/18 | + | | M 11/18 |
|- | |- | ||
− | | 11/20 | + | | W 11/20 |
|- | |- | ||
− | | 11/25 | + | | M 11/25 |
|- | |- | ||
− | | 11/27 || ''Thanksgiving - class cancelled'' || | + | | W 11/27 || ''Thanksgiving - class cancelled'' || |
|- | |- | ||
− | | 12/2 | + | | M 12/2 |
|- | |- | ||
− | | 12/4 | + | | W 12/4 |
|} | |} | ||
Revision as of 15:48, 31 July 2013
This is the syllabus for Machine Learning 10-601 in Fall 2013.
Contents
Prezi Overview of All the Topics in the Course
Schedule
Date | Topic | Lecturer | What's due | What's new |
---|---|---|---|---|
M 9/2 | No class - Labor day | |||
W 9/4 | Overview and Intro to Probability | William | ||
M 9/9 | The Naive Bayes algorithm | William | ||
W 9/11 | Logistic Regression | William | ||
M 9/16 | The Perceptron algorithm | William | ||
W 9/18 | Evaluating and comparing classifiers | William | ||
M 9/23 | Neural networks | Eric | ||
W 9/25 | K-nearest neighbor classifiers | Eric | ||
M 9/30 | Decision trees | Eric | ||
W 10/2 | PAC-learning and learning theory | William | ||
M 10/7 | Bias-variance and linear regression | William | ||
W 10/9 | K-means and Mixture models | Eric | ||
M 10/14 | Dimensionality Reduction | Eric | ||
W 10/16 | Modeling distributions with Bayes Nets and Markov Fields | Eric | ||
M 10/21 | Learning with graphical models | Eric | ||
W 10/23 | Topic models | William | ||
M 10/28 | HMMs | Eric | ||
W 10/30 | ||||
M 11/4 | ||||
W 11/6 | ||||
M 11/11 | ||||
W 11/13 | ||||
M 11/18 | ||||
W 11/20 | ||||
M 11/25 | ||||
W 11/27 | Thanksgiving - class cancelled | |||
M 12/2 | ||||
W 12/4 |
Section-by-Section
Linear Classifiers
A probabilistic view of linear classification:
Another view of classification:
- 10-601 Introduction to Linear Algebra
- 10-601 Perceptrons and Voted Perceptrons
- 10-601 Voted Perceptrons and Support Vector Machines
Summary: