ECE365: Fundamentals of Machine Learning (Logistics)

Contact Information

Instructor: Professor Venugopal V Veeravalli
Office: 315 Coordinated Science Laboratory
Email: vvv [at] illinois [dot] edu
Office Hours: By Appointment

Teaching Assistant: Aditya Deshmukh
Email: ad11 [at] illinois [dot] edu
Office Hours: Fridays, 3:00 PM - 4:00 PM, 2036 ECEB


There is no required textbook – we will provide a set of notes [here]. The notes contain pointers to relevant references. Some general references are given below.

Lab Submission: Submit your completed Jupyter notebook (name it netid.ipynb) on Compass. If your code depends on any files not provided for the lab, then also upload those (in file). Be sure to fill in your name + netid at the top of the lab. Do not send me the data sets!

Grading: You will have a weekly quiz on Thursday (except for the first week of class). These quizzes are short (approximately 20 minutes) and are designed to test the concepts you have learned. The quizzes are closed-book and closed-notes. You may bring a ruler. Electronic devices (calculators, cellphones, pagers, laptops, headphones, etc.) are neither necessary nor permitted. The quizzes form 30% of your grade. No collaboration is allowed during the quizzes. The labs will form the remaining 70% of your grade. Each lab will be weighted equally. If you have a request for re-grading, the request must be submitted in writing within a week of the lab being returned to you. It should have a clear explanation of what you would like to be looked at again. Grades will be posted on Compass].

Late Policy: No Late submission will be graded this year!

There are no exceptions to these policies beyond the standard policies of the university (e.g. disability accomodations, serious illness, etc.). If you need an exception, please contact Prof. Veeravalli.

These policies apply only to the “Fundamentals of Machine Learning” section of the course.

General References

You do not need any of the following books, but they may be useful to expand on some of the topics seen in class. Most of the course material is covered in the first book. The second book is essentially a simplified version of the first book.

  • Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning (2nd Edition). Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2008. [link] (Free!)

  • Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2013. [link] (Free!)

  • Sanjeev Kulkarni, Gilbert Harman. An Elementary Introduction to Statistical Learning Theory. John Wiley & Sons, 2011. [link]

  • Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.

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

  • Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2nd Edition). Wiley-Interscience, 2000.

  • Sergios Theodoridis and Konstantinos Koutroumbas, Pattern Recognition (4th Edition). Academic Press, 2009. [link] (UIUC only)

  • Larry Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer Publishing Company, Incorporated, 2010. [link] (UIUC only)