DS 5220 / CS6140-02

Supervised Machine Learning

Fall 2017

TF 9:50am - 11:30am, Forsyth 236

Instructor: Olga Vitek

Email: o.vitek@neu.edu

Office hours: WVH 310F, Tuesdays 10am-11:000am, by appointment.

Phone: (617) 373-6305

Mailbox: WVH 202

Teaching assistant: Mrs. Dan Guo

Email: guo.dan@husky.neu.edu

Office hours: WVH 310, TBD.

Admin: Syllabus, Piazza, Blackboard. Academic integrity.

Required texts:

    [HTF] Elements of Statistical Learning. T. Hastie, R. Tibshirani and J. Friedman, Springer, 2009.

    [JWHT] An Introduction to Statistical Learning. G. James, D. Witten, T. Hastie, R. Tibshirani,

                Springer 2013

Optional texts:

    Machine Learning: A Probabilistic Perspective. Kevin P. Murphy, MIT Press 2012.

    Pattern Classification, 2nd Edition. R. O. Duda, P. E. Hart, D. Stork, Wiley and Sons, 2001.

    Pattern Recognition and Machine Learning. C. M. Bishop, Springer 2006.

    Machine Learning. T. Mitchell, McGraw-Hill, 1997.

1. Introduction.

Fri, Sep 8:

2. Probability review.

Tue, Sep 12:

Fri, Sep 15:

3. Linear regression. Model assessment and selection.

Tue, Sep 19:

Fri, Sep 22:

***Tue, Sep 26:

4. Logistic regression.

Fri, Sep 29:

Tue, Oct 3:

5. Generative classifiers.

Fri, Oct 6:

Tue, Oct 10:

Fri, Oct 13:

***Tue, Oct 17:

***Fri, Oct 20: Midterm exam.

6. Splines and kernels. Support vector machines.

Tue, Oct 24:

Fri, Oct 27:

Tue, Oct 31:

7. Tree-based and ensemble methods.

Fri, Nov 3:

Tue, Nov 7:

Fri, Nov 10:

8. Neural networks.

Tue, Nov 14:

***Fri, Nov 17:

Tue, Nov 21:

Fri, Nov 24: No class, Thanksgiving.

9. Advanced topics.

Tue, Nov 28:

Fri, Dec 1:

Tue, Dec 5:

Fri, Dec 8:

Tue, Dec 12: Final exam.

Tentative schedule and handouts