Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2014"
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| Wed 10/1 (Ziv) || Thur 10/2 ('''Ziv''') || SVMs and Margin Classifiers 2 || | | Wed 10/1 (Ziv) || Thur 10/2 ('''Ziv''') || SVMs and Margin Classifiers 2 || | ||
|- | |- | ||
| − | | Mon 10/6 (Ziv) || Tues 10/7 (Wm) || [[10-601 Evaluation|Evaluating and Comparing Classifiers Experimentally]] || HW6: Comparing classifiers (Experiments, | + | | Mon 10/6 (Ziv) || Tues 10/7 (Wm) || [[10-601 Evaluation|Evaluating and Comparing Classifiers Experimentally]] || HW6: Comparing classifiers (Experiments, experiments with Weka) |
|- | |- | ||
| Wed 10/8 (Ziv) || Thus 10/9 (Wm) || [[10-601 PAC| PAC Learning]] || ''wiki page to be updated'' | | Wed 10/8 (Ziv) || Thus 10/9 (Wm) || [[10-601 PAC| PAC Learning]] || ''wiki page to be updated'' | ||
Revision as of 09:52, 1 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 | HW1: Probability (Worksheet) |
| 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 / HW2: Decision Tree (Worksheet) |
| Wed 9/10 (Ziv) | Thur 9/11 (Wm) | The Naive Bayes algorithm | |
| Mon 9/15 (Ziv) | Tues 9/16 (Wm) | Linear Regression | slides will be updated / HW3: Naive Bayes (Programming) |
| Wed 9/17 (Ziv) | Thur 9/18 (Wm) | Logistic Regression | |
| Mon 9/22 (Wm) | Tues 9/23 (Wm) | The Perceptron algorithm | HW4: Logistic Regression (Programming) |
| Wed 9/24 (Ziv) | Thur 9/25 (Wm) | Neural networks and Deep Belief Networks | slides will be updated |
| Mon 9/29 (Ziv) | Tues 9/30 (Ziv) | SVMs and Margin Classifiers 1 | HW5: Neural nets worksheet |
| 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 | HW6: Comparing classifiers (Experiments, experiments with Weka) |
| Wed 10/8 (Ziv) | Thus 10/9 (Wm) | PAC Learning | wiki page to be updated |
| Mon 10/13 (Wm) | Tues 10/14 (Wm) | Bias-Variance Decomposition | wiki page to be updated / HW7: Pac-learning (worksheet) |
| Wed 10/16 (Ziv) | Thur 10/16 (Wm) | Ensemble Methods 1, Ensemble Methods 2 | slides to be updated |
| Mon 10/20 (Ziv) | Tues 10/21 (Wm) | Unsupervised Learning: k-Means and Mixtures | potential guest lecture / HW8: k-means (programming) |
| 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 Practice exam distributed |
| Thurs 10/30 | Thur 10/30 | Mid-term Exam | TBA: room and/or time may be different |
| Mon 11/3 (Ziv) | Tues 11/4 (Ziv) | Graphical Models 1 | wiki to be updated |
| 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 | HW9: Graphical Models (worksheet) |
| 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 | HW10: HMM/Topic models (worksheet, experiments with LDA code) |
| 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 | |
| Tues 12/2 (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.
