Note: this syllabus is tentative and subject to change. Book readings refer to the books listed below, unless otherwise noted. Some readings are available from the library's electronic reserves, for which you will need a userID and password that were distributed via email. (Contact the course staff if you need them to be re-sent to you.) You also can get these from blackboard using your OIT userID and password.
| Tu, 2/6 | Introduction | scribe notes | |
| Th, 2/8 | Probability and statistics review | scribe notes ; slides | |
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| Tu, 2/13 | Introduction to classification and the K-nearest-neighbor algorithm | scribe
notes Mitchell, pp.230-236 |
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| Th, 2/15 | High dimensional space | scribe notes Sections 9.5-9.6 of Coding and Information Theory (2nd ed, 1986) by R.W. Hamming |
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| Decision trees | Mitchell, Chapter 3 | ||
| Tu, 2/20 | Computational learning theory | scribe notes | |
| Th, 2/22 | Boosting | scribe notes | |
| Tu, 2/27 | Support vector machines |
scribe notes Sections 5.4-5.7 of The nature of statistical learning theory (1995) by V.N. Vapnik | Burges's tutorial on svm's |
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| Th, 3/1 | K-means clustering | scribe notes;
slides
Hastie et al., Sections 14.1-14.3 (on e-reserve or blackboard) |
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| Tu, 3/6 | Agglomerative clustering | scribe notes ; slides | |
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| Th, 3/8 | Introduction to graphical models | scribe notes | Jordan's Statistical Science paper |
| Tu, 3/13 | Naive Bayes classification | case study slides ; scribe notes | |
| Th, 3/15 | Mixture models, latent variable models and EM | scribe notes; "Introduction to Graphical Models" 10.1, 11 (handout outside CS-204) |
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| Tu, 3/27 | Expectation-maximization | scribe notes | |
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| Th, 3/29 Tu, 4/3 | Introduction to regression; Linear regression | scribe notes 3/29 scribe notes 4/3 Hastie et al., pp. 41-45, 55-65, 115-120 (on e-reserve or blackboard); Bishop, pp. 137-152, except sections 3.1.2 and 3.1.5 (there is some overlap between these readings that you can feel free to skim) |
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| Th, 4/5 | Logistic regression |
scribe notes Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression (new, unpublished chapter of the Mitchell book) |
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| Tu, 4/10 Th, 4/12 | Principal components analysis |
scribe notes 4/10; scribe notes 4/12; Hastie et al., sections 14.5-14.6 (on e-reserve or blackboard) |
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| Th, 4/19 | Factor analysis | scribe notes 4/19 ; MV Gaussian examples; Jordan Ch. 13-14 (outside of Dave's door) | |
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| Tu, 4/17 | Maximum entropy modeling (RS) |
scribe notes "A maximum entropy approach to species distribution modeling" | slides; paper on modeling "bake-off" |
(RS)
| Tu, 4/24 | Applications to computational biology (guest lecture by Prof. Olga Troyanskaya) | scribe notes (draft); slides | |
| Th, 4/26 | Applications to computer vision (guest lecture by Prof. Fei-Fei Li) | scribe notes ; slides | |
| Tu, 5/1 | Computational neuroscience and fMRI data (guest lecture by Prof. Ken Norman) | scribe
notes ; slides; "Beyond mind-reading: multi-voxel pattern analysis of fMRI data" | |
| Th, 5/3 | Topic models: Hidden variable models for large document collections (and class summary) (DB) | scribe notes ; slides |