Instructor: Dimitrios Katselis, Email: katselis@illinois.edu
TA: Yifeng Chu, Email: ychu26@illinois.edu
Schedule: TR 2-3:20pm, ECEB 3013
Office Hours: Dimitris: W 4:00-5:00pm, on zoom
Prerequisites: ECE 534 (Random Processes)
Main References
Lecture notes by Profs. Bruce Hajek and Maxim Raginsky
Lecture notes by Prof. R. Srikant
Additional References
S. Shalev-Shwartz and S. Ben David, Understanding Machine Learning: From Theory to Algorithms
M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning
Vladimir N. Vapnik, Statistical Learning Theory
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining,
Inference, and Prediction
Homework (60%), Scribing (10%), Final Project (30%)
Homework: You can discuss the homework problems in general terms.
Nevertheless, identical solutions (copying) will
not be allowed.
The solutions will be submitted on gradescope. The homework is due on May 1, 11:59pm. Late homework
will not be accepted.
Final Project: Similarly to Spring 2021 (see the course webpage by Prof. Raginsky below), you may select and evaluate
1 paper relevant to
the course material from conferences such as COLT, ICML, ALT, AISTATS and NeurIPS. Moreover,
you should work in groups of preferably 4 students. A brief project proposal from each group
is due by Fri, April 12, 11:59pm.
NOTE: You are not expected to put effort on deriving new results or extensions based on the selected
paper. To control the course workload and to also ensure a concrete and streamlined framework for
the grading of your final projects, you should restrict your presentations only to the results appearing
in the selected paper by your groups.
Please check past offerings of the course by Profs. Hajek, Srikant and Raginsky: