ECE 365: Fundamentals of Machine Learning (Logistics)

Contact Information

Instructor: Professor Venugopal V. Veeravalli
Email: vvv [at] illinois [dot] edu
Office Hours: By appointment through email only
Zoom Link: here

Teaching Assistant: Yuchen Liang
Email: yliang35 [at] illinois [dot] edu
Office Hours: Same as lab hours
Zoom Link: here


There is no required textbook. The typed notes are here. The notes contain pointers to relevant references. Some general references are given below.

Lab Submission: Fill in the given template python file and upload to gradescope. An autograder will grade your submission. You also need to answer all short answer questions on gradescope. Do not send us the data sets! Gradescope entry code is N843KK. Please change your gradescope Student ID to your University UIN.

Grading: You will have two quizzes. The first quiz covers material from Lectures 1 to 5. The second quiz covers material from Lectures 6 to 10. The quizzes are closed-book and closed-notes. Electronic devices (calculators, cellphones, pagers, laptops, headphones, etc.) are neither necessary nor permitted. The quizzes constitute 30% of your grade. The labs will constitute 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 gradescope.

Late Policy: No Late submission will be graded!

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)