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Proficiency in the material covered in CS 760 is assumed even though the material may not be explicitly listed in the reading list below. Content covered in 760 lectures is important to understand the readings as well as to answer the qualifier questions.

Foundations of Machine Learning

Tom Mitchell (1997). Machine Learning, McGraw-Hill. [Chapters 1-8, 10, 13]

Bias-Variance Tradeoff

Stuart Geman, Elie Bienenstock and Rene Doursat (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1–58. [Sections 1-3]

Precision-Recall and ROC Curves

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze (2008). Introduction to Information Retrieval, Cambridge University Press. 2008. [Sections 8.3-8.4]

Ensemble Methods

Thomas G. Dietterich (1997). Machine Learning Research: Four Current Directions. AI Magazine, 18: 97-136. [Sections 1-2]

Active Learning

Burr Settles (2012). Active Learning. [Sections 1-3]

Support Vector Machines

Asa Ben-Hur and Jason Weston (2010). A User’s Guide to Support Vector Machines. In Data Mining Techniques for the Life Sciences: Methods in Molecular Biology, 2010, Volume 609, Part 2, Chapter 13, pp. 223-239.

Semi-Supervised Learning

Xiaojing Zhu and Andrew Goldberg (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool, 2012. [Chapters 1, 2, 5].

Statistical Relational Learning

Pedro Domingos and Matt Richardson (2007). Markov Logic: A Unifying Framework for Statistical Relational Learning. In L. Getoor and B. Taskar (eds.), Introduction to Statistical Relational Learning, Cambridge, MA: MIT Press, Chapter 12.

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Page last modified on May 10, 2012, at 01:55 PM