Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
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| 9/16 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William | | 9/16 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William | ||
|- | |- | ||
− | | 9/18 | + | | 9/18 || Evaluating and comparing classifiers || William |
|- | |- | ||
− | | 9/23 | + | | 9/23 || Neural networks || Eric |
|- | |- | ||
− | | 9/25 | + | | 9/25 || K-nearest neighbor classifiers || Eric |
|- | |- | ||
− | | 9/30 | + | | 9/30 || Decision trees || Eric |
|- | |- | ||
− | | 10/2 | + | | 10/2 || PAC-learning and learning theory || William |
|- | |- | ||
− | | 10/7 | + | | 10/7 || Bias-variance and linear regression || William |
|- | |- | ||
− | | 10/9 | + | | 10/9 || K-means and Mixture models || William |
|- | |- | ||
− | | 10/14 | + | | 10/14 || Dimensionality Reduction || Eric |
|- | |- | ||
− | | 10/16 | + | | 10/16 || Modeling distributions with Bayes Nets and Markov Fields || Eric |
|- | |- | ||
− | | 10/21 | + | | 10/21 || Learning with graphical models || Eric |
|- | |- | ||
− | | 10/23 | + | | 10/23 || Topic models || William |
|- | |- | ||
− | | 10/28 | + | | 10/28 || HMMs || Eric |
|- | |- | ||
| 10/30 | | 10/30 |
Revision as of 15:43, 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 | Due assignment | New assignment |
---|---|---|---|---|
9/2 | No class - Labor day | |||
9/4 | Overview and Intro to Probability | William | ||
9/9 | The Naive Bayes algorithm | William | ||
9/11 | Logistic Regression | William | ||
9/16 | The Perceptron algorithm | William | ||
9/18 | Evaluating and comparing classifiers | William | ||
9/23 | Neural networks | Eric | ||
9/25 | K-nearest neighbor classifiers | Eric | ||
9/30 | Decision trees | Eric | ||
10/2 | PAC-learning and learning theory | William | ||
10/7 | Bias-variance and linear regression | William | ||
10/9 | K-means and Mixture models | William | ||
10/14 | Dimensionality Reduction | Eric | ||
10/16 | Modeling distributions with Bayes Nets and Markov Fields | Eric | ||
10/21 | Learning with graphical models | Eric | ||
10/23 | Topic models | William | ||
10/28 | HMMs | Eric | ||
10/30 | ||||
11/4 | ||||
11/6 | ||||
11/11 | ||||
11/13 | ||||
11/18 | ||||
11/20 | ||||
11/25 | ||||
11/27 | Thanksgiving - class cancelled | |||
12/2 | ||||
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: