Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
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| M 9/30 || Decision trees || Eric | | M 9/30 || Decision trees || Eric | ||
|- | |- | ||
− | | W 10/2 || PAC-learning and learning theory || William | + | | W 10/2 || PAC-learning and learning theory || William || HW: worksheet on theory |
|- | |- | ||
− | | M 10/7 || Bias-variance and linear regression || William | + | | M 10/7 || Bias-variance and linear regression || William |
|- | |- | ||
| W 10/9 || K-means and Mixture models || Eric | | W 10/9 || K-means and Mixture models || Eric | ||
|- | |- | ||
− | | M 10/14 || Dimensionality Reduction || Eric | + | | M 10/14 || Dimensionality Reduction || Eric |
|- | |- | ||
− | | W 10/16 || Modeling distributions with Bayes Nets and Markov Fields || Eric | + | | W 10/16 || Modeling distributions with Bayes Nets and Markov Fields - 1 || Eric |
|- | |- | ||
− | | M 10/21 || | + | | M 10/21 || Modeling distributions with Bayes Nets and Markov Fields - 2 || Eric |
|- | |- | ||
− | | W 10/23 | + | | W 10/23 |
|- | |- | ||
− | | M 10/28 || | + | | M 10/28 || Topic models || William |
|- | |- | ||
− | | W 10/30 | + | | W 10/30 || HMMs || Eric |
|- | |- | ||
| M 11/4 | | M 11/4 |
Revision as of 15:58, 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 | Assignment |
---|---|---|---|
M 9/2 | No class - Labor day | ||
W 9/4 | Overview and Intro to Probability | William | HW: worksheet on probabilities |
M 9/9 | The Naive Bayes algorithm | William | |
W 9/11 | The Perceptron algorithm | William | HW: Implement two learners |
M 9/16 | Logistic Regression | William | |
W 9/18 | Evaluating and comparing classifiers | William | HW: Implement logistic regression |
M 9/23 | Neural networks | Eric | |
W 9/25 | K-nearest neighbor classifiers | Eric | HW: Compare several classifiers |
M 9/30 | Decision trees | Eric | |
W 10/2 | PAC-learning and learning theory | William | HW: worksheet on theory |
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 - 1 | Eric | |
M 10/21 | Modeling distributions with Bayes Nets and Markov Fields - 2 | Eric | |
W 10/23 | |||
M 10/28 | Topic models | William | |
W 10/30 | HMMs | Eric | |
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: