Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2014"
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| Wed 10/15 (Ziv) || Thur 10/16 (Wm) || [[10-601 Ensembles 1|Ensemble Methods 1]], [[10-601 Ensembles 2|Ensemble Methods 2]]|| ''slides to be updated'' || HW5: Pac-learning (worksheet) - due 10/27 | | Wed 10/15 (Ziv) || Thur 10/16 (Wm) || [[10-601 Ensembles 1|Ensemble Methods 1]], [[10-601 Ensembles 2|Ensemble Methods 2]]|| ''slides to be updated'' || HW5: Pac-learning (worksheet) - due 10/27 | ||
|- | |- | ||
| − | | Mon 10/20 (Ziv) || Tues 10/21 (Wm) || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || ''potential guest lecture'' || | + | | Mon 10/20 (Ziv) || Tues 10/21 (Wm) || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || ''potential guest lecture'' || Practice exam distributed |
|- | |- | ||
| Wed 10/22 (Ziv) || Thur 10/23 (Wm) || [[10-601 DR| Unsupervised Learning: Dimensionality Reduction]]|| || | | Wed 10/22 (Ziv) || Thur 10/23 (Wm) || [[10-601 DR| Unsupervised Learning: Dimensionality Reduction]]|| || | ||
|- | |- | ||
| − | | Mon 10/27 (Ziv) || Tues 10/28 (Wm) || Review session || ''slides to be posted'' || | + | | Mon 10/27 (Ziv) || Tues 10/28 (Wm) || Review session || ''slides to be posted'' || |
|- | |- | ||
| Wed 10/29 || Wed 10/29 || '''Mid-term Exam''' || ''TBA: tentatively 7-9pm, room and/or time may be different'' | | Wed 10/29 || Wed 10/29 || '''Mid-term Exam''' || ''TBA: tentatively 7-9pm, room and/or time may be different'' | ||
Revision as of 11:46, 5 August 2014
This is the syllabus for Machine Learning 10-601 in Fall 2014.
Schedule
| Lecture for 601-A | Lecture for 601-B | Topic | Notes | Assignment |
|---|---|---|---|---|
| Wed 8/27 (Ziv) | Tues 9/2 (Wm) | Course Overview and Introduction to Probability | ||
| Wed 9/3 (Ziv) | Thur 9/4 (Wm) | Classification and K-NN | slides will be updated | |
| Mon 9/8 (Ziv) | Tues 9/9 (Wm) | Decision Trees, and Rule Learning | slides will be updated | |
| Wed 9/10 (Ziv) | Thur 9/11 (Wm) | The Naive Bayes algorithm | HW1: KNN and Decision Trees (Worksheet) - due 9/18 | |
| Mon 9/15 (Ziv) | Tues 9/16 (Wm) | Linear Regression | slides will be updated | |
| Wed 9/17 (Ziv) | Thur 9/18 (Wm) | Logistic Regression | HW2: Naive Bayes, Linear Regression (Matlab Programming) - due 9/25 | |
| Mon 9/22 (Wm) | Tues 9/23 (Wm) | The Perceptron algorithm | ||
| Wed 9/24 (Ziv) | Thur 9/25 (Wm) | Neural networks and Deep Belief Networks | HW3: Logistic Regression, Neural networks (Matlab Programming) - due 10/9 | |
| Mon 9/29 (Ziv) | Tues 9/30 (Ziv) | SVMs and Margin Classifiers 1 | ||
| Wed 10/1 (Ziv) | Thur 10/2 (Ziv) | SVMs and Margin Classifiers 2 | ||
| Mon 10/6 (Ziv) | Tues 10/7 (Wm) | Evaluating and Comparing Classifiers Experimentally | ||
| Wed 10/8 (Ziv) | Thus 10/9 (Wm) | PAC Learning | wiki page to be updated | HW4: SVM, Comparing classifiers (Experiments with Weka) - due 10/16 |
| Mon 10/13 (Wm) | Tues 10/14 (Wm) | Bias-Variance Decomposition | wiki page to be updated | |
| Wed 10/15 (Ziv) | Thur 10/16 (Wm) | Ensemble Methods 1, Ensemble Methods 2 | slides to be updated | HW5: Pac-learning (worksheet) - due 10/27 |
| Mon 10/20 (Ziv) | Tues 10/21 (Wm) | Unsupervised Learning: k-Means and Mixtures | potential guest lecture | Practice exam distributed |
| Wed 10/22 (Ziv) | Thur 10/23 (Wm) | Unsupervised Learning: Dimensionality Reduction | ||
| Mon 10/27 (Ziv) | Tues 10/28 (Wm) | Review session | slides to be posted | |
| Wed 10/29 | Wed 10/29 | Mid-term Exam | TBA: tentatively 7-9pm, room and/or time may be different | |
| Mon 11/3 (Ziv) | Tues 11/4 (Ziv) | Graphical Models 1 | wiki to be updated | HW6: unsupervised learning (programming) - due 11/10 |
| Wed 11/5 (Ziv) | Thur 11/6 (Ziv) | Graphical Models 2 | wiki to be updated | |
| Mon 11/10 (Ziv) | Tues 11/11 (Ziv) | HMMS and Sequences | HW7: HMMS and Graphical Models (worksheet) - due 11/17 | |
| Wed 11/13 (Wm) | Thur 11/13 (Wm) | Matrix Factorization and Topic Models | slides to be updated | |
| Mon 11/17 (Wm) | Tues 11/18 (Wm) | Network Models | HW8: Topic models (worksheet, experiments with LDA code) - due 11/24 | |
| Wed 11/19 (Wm) | Thur 11/20 (Wm) | Semi-supervised learning | possible guest lecture | |
| Mon 11/24 (Wm) | Tues 11/25 (Wm) | Scalable Learning and Parallelization | Project milestone 1 (Evaluating/reporting on Weka classifiers) | |
| Wed 11/26 | Thur 11/27 | No class - Thanksgiving | ||
| Mon 12/1 (Wm) | Tues 12/2 (Wm) | Learning and NLP | slides to be updated | Project milestone 2 (Combining Weka classifiers) |
| Wed 12/3 (Ziv) | Thurs 12/4 (Ziv) | Learning and Biology | ||
| Mon 12/8 | Project due (Final experiments and writeup) |
To other instructors: if you'd like to use any of the materials found here, you're absolutely welcome to do so, but please acknowledge their ultimate source somewhere.
