Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2014"
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| (Mon +) Tues 10/14 || [[10-601 Bias-Variance|Bias-Variance Decomposition]] || William || William's also lecturing in Ziv's class on Mon | | (Mon +) Tues 10/14 || [[10-601 Bias-Variance|Bias-Variance Decomposition]] || William || William's also lecturing in Ziv's class on Mon | ||
|- | |- | ||
| − | | Thurs 10/16 || [[10-601 Ensembles 1|Ensemble Methods 1]] || William || | + | | Thurs 10/16 || [[10-601 Ensembles 1|Ensemble Methods 1]], [[10-601 Ensembles 2|Ensemble Methods 2]]|| William || ''slides to be updated'' |
|- | |- | ||
| Tues 10/21 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Bhavana or Ziv || | | Tues 10/21 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Bhavana or Ziv || | ||
Revision as of 16:25, 11 July 2014
This is the syllabus for Machine Learning 10-601 in Fall 2014.
Schedule
| Date of lecture | Topic | Lecturer | Assignment/Notes | |
|---|---|---|---|---|
| Tues 9/2 | Overview and Intro to Probability | William | ||
| Thurs 9/4 | Classification and K-NN | William | slides will be updated | |
| Tues 9/9 | Decision Trees, and Rule Learning | William | slides will be updated | |
| Thurs 9/11 | The Naive Bayes algorithm | William | ||
| Tues 9/16 | Linear Regression | William | slides will be updated | |
| Thurs 9/18 | Logistic Regression | William | ||
| (Mon +) Tues 9/23 | The Perceptron algorithm | William | William's also lecturing in Ziv's class on Mon | |
| Thurs 9/25 | Neural networks and Deep Belief Networks | William | slides will be updated | |
| (Mon +) Tues 9/30 | SVMs and Margin Classifiers 1 | Ziv | Ziv's also lecturing in his class on Mon | |
| (Wed +) Thurs 10/1-2 | SVMs and Margin Classifiers 2 | Ziv | Ziv's also lecturing in his class on Wed | |
| Tues 10/7 | Evaluating and Comparing Classifiers Experimentally | William | ||
| Thus 10/9 | PAC Learning | William | ||
| (Mon +) Tues 10/14 | Bias-Variance Decomposition | William | William's also lecturing in Ziv's class on Mon | |
| Thurs 10/16 | Ensemble Methods 1, Ensemble Methods 2 | William | slides to be updated | |
| Tues 10/21 | Unsupervised Learning: k-Means and Mixtures | Bhavana or Ziv | ||
| Thus 10/23 | Unsupervised Learning: Dimensionality Reduction | Bhavana or Ziv | ||
| Tues 10/28 | Review session | William | slides to be posted | |
| Thurs 10/30 | mid-term exam | n/a | TBA: room and/or time may be different | |
| Tues 11/4 | Graphical Models 1 | Ziv | ||
| Thurs 11/6 | Graphical Models 2 | Ziv | HW7: Graphical Models (due Nov. 13th before class via BlackBoard) | |
| M 11/11 | HMMS, Sequences, and Structured Output Prediction | William | ||
| W 11/13 | d-separation, Explaining away, and Topic Models | William | Project milestone 4 | |
| M 11/18 | Network Models | Eric | ||
| W 11/20 | Review Session/Special Topics | Eric | ||
| M 11/25 | Not-quite-final Exam | |||
| W 11/27 | No class - Thanksgiving | |||
| M 12/2 | Markov Decision Processes and Reinforcement Learning | Eric | ||
| W 12/4 | Scalable Learning and Parallelization | William | Milestones 5-6 Description | |
| Mon 12/9 | Milestone 5 due | |||
| Tue 12/10 | Milestone 6 (writeup) due |
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.
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:
