ECE398BD: Fundamentals of Machine Learning (Logistics)

Contact Information

Instructor: Professor Yoram Bresler
Office: 112 Coordinated Science Laboratory
Email: ybresler [at] illinois [dot] edu
Office Hours: By Appointment

Teaching Assistant: G. Rovatsos
Email: rovatso2 [at] illinois [dot] edu
Office Hours: Mondays 4PM-5PM, ECEB 3020


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: To upload your lab solution to Box, email (as an attachment) your completed IPython notebook (name it netid.ipynb) to Be sure to use your illinois email account. If your code depends on any files not provided for the lab, then also upload those by including as an attachment in your email. Your email may include multiple attachments, but several emails, each with its own attachment, are OK too. Be sure to fill in your name + netid at the top of your completed IPython notebook. You should recieve a confirmation email from Box after submission (if you do not see it, check your spam folder). If you do not see it after a half hour, email the lab (i.e., send the email with the same attachments described above, which you sent to Box) to rovatso2 [at] illinois [dot] edu from your Illinois email account with the subject ECE398BD-NetID-Lab#. Use the same filenames you used in maling to Box. Do not send me (nor to Box) data sets that are provided to you! I'd prefer if you submitted your lab once, but if you have to resubmit a modified version, please rename it netid_v#.ipynb, where # is the number of resubmissions you have done. For example, my first submission to Box would be rovatso2.ipynb, and if I needed to resubmit a modified version, I would use rovatso2_v2.ipynb.

Grading: You will have a weekly quiz on Tuesdays (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: We are providing you with a total budget of 32 hours of lateness, which you may split between the four graded labs. If a lab is turned in late, we will deduct the number of hours your lab is late (rounded up) from the budget. If you have used up your budget, your assignment will not be accepted for grading. You will have no point deductions for being late, provided you have not used up your budget. For example, if you turn in Lab 2 ten minutes late, you will have 31 hours left in your budget for the remaining labs.

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. Bresler.

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!)

  • 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)