Machine Learning, COM-3920-341
Spring 2019,
TR: 4:30 - 5:45
Tentative Schedule
| Topics | Readings | |
|---|---|---|
| Week 1 | Course overview [VIDEO] Overview of Machine Learning: history, relation to classical statistics Text: Chapter 1; Slides: Welcome, Introduction |
|
| Week 2 | Beginners Guide to Regression Analysis and Plot Interpretations | |
| Week 3 | ||
| Week 4 |
Support Vector Machines: uses in regression and classification, optimization, choice of parameter C, probabilistic interpretation
Kernel Methods: RBF kernels, Mercer kernels, Matern kernels, Linear kernels, String kernels, Kernels derived from probabilistic generative models; the “kernel trick” and its uses: nearest neighbor classification, K-medoids clustering, ridge regression, and PCA Text: Chapter 14.1, 14.2, 14.5, supplemental notes by Ng Slides: Support Vector Machines |
Other textbooks:
-
Bayesian Reasoning and Machine Learning -
PDF
- A Course in Machine Learning by Hal Daume III
- G. James, D. Witten, T. Hastie, and R. Tibshirani. (2013) An Introduction to Statistical Learning - with Applications in R . Springer.
- T. Hastie, R. Tibshirani, and J. Friedman. (2009) The Elements of Statistical Learning - Data Mining, Inference, and Prediction (2nd Edition) . Springer.
Podcasts: Other Useful References:
