# ECE 598MR: Statistical Learning Theory (Fall 2013)

### References

There is no required textbook for this class; however, the following three books are useful:
- Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar,
*Foundations of Machine Learning*, MIT Press, 2012 [A comprehensive first look, discusses Rademacher complexities]
- Luc Devroye, László Györfi, and Gábor Lugosi,
*A Probabilistic Theory of Pattern Recognition*, Springer, 1996 [Focuses primarily on binary classification]
- Felipe Cucker and Ding Xuan Zhou,
*Learning Theory: An Approximation Theory Viewpoint* Cambridge, 2007 [Focuses primarily on regression and kernel methods]

Here are some additional survey papers that I recommend (more will be added as the class progresses):
#### Concentration inequalities

#### Binary classification

- Olivier Bousquet, Stéphane Boucheron, and Gábor Lugosi, Theory of classification: a survey of recent advances,
*ESAIM Probability and Statistics*, vol. 9, pp. 323-375, 2005
- Sanjeev Kulkarni, Gábor Lugosi, and Santosh Venkatesh, Learning pattern classification -- a survey,
*IEEE Transactions on Information Theory*, vo. 44, no. 6, pp. 2178-2206, 1998

#### Regression

- Felipe Cucker and Steve Smale, On the mathematical foundations of learning,
*Bulletin of the American Mathematical Society*, vol. 39, no. 1, pp. 1-49, 2001
- Tomaso Poggio and Steve Smale, The mathematics of learning: dealing with data,
*Notices of the American Mathematical Society*, vol. 50, no. 5, pp. 537-544, 2003