ECE
516: Adaptive Digital Filters
Prof. Mojtaba Soltanalian (msol@uic.edu)
We are easing ourselves back to campus through a type of flipped
classroom approach. The First Lecture: We will gather to discuss the
syllabus and to get to know each other on Monday, August 23rd,
9:30-10:45am. Enrolled students are encouraged to watch the first lecture on Google Drive before this gathering. The Paper Presentations: Lectures typically consume
about 2/3 of the allocated time, while 1/3 of the time is dedicated to student
paper presentations, in order to strengthen familiarity of the course
participants with various applications of the discussed ideas as well as to
deepen our fundamental understanding of the concepts. The required student
paper presentations will be scheduled on Wednesdays (max 2 in each session) and
slides will be shared with other students via Google
Drive beforehand. You are strongly encouraged to participate in
presentations or at least read through the slides, as the ideas discussed in
such presentations may be considered in exam questions. If you are off campus,
you may wish to listen in through the presentations via Webex at the time they are
happening. The Project Presentations: The last four sessions of the
course are dedicated to project presentations, on which you will receive more information
in due time. These sessions will be held on campus. Masking Requirement: Per university policy,
face masks will be required indoors for everyone on campus, including in
classrooms. Useful Links: * Adaptive Filters, by Ali H. Sayed, John Wiley & Sons, NJ,
2008, available in hard copy at the UIC Bookstore. Other useful references: - Simon Haykin, Adaptive Filter Theory,
Fifth edition, Prentice Hall, 2013. - Torsten Soderstrom, and Petre Stoica. System identification.
Prentice-Hall, 1988. Available online: http://user.it.uu.se/~ps/sysidbook.pdf Date To be covered Further info. 8/23 8/25 8/30 Mean-Square
Error Estimation 9/1 Mean-Square
Error Estimation 9/6 No
Classes 9/8 Linear
Estimation 9/13 Linear
Models and Applications 9/15 Linear
Models and Applications 9/20 Constrained
Estimation and Applications 9/22 Kalman
Filtering 9/27 Review
Class / Extended Office Hours 9/29 Midterm
1 10/4 Steepest-Descent
Algorithms 10/6 Stochastic-Gradient
Algorithms 10/11 No
Classes 10/13 Other
Local Algorithms 10/18 Least-Squares
Methods 10/20 Recursive
Least-Squares 10/25 LS
and RLS (continued) 10/27 Review
Class / Extended Office Hours 11/1 No
Classes 11/3 Midterm
2 Deadline
to select project topic 11/8 Guaranteed
Adaptation 11/10 Tracking
Performance of Adaptive Filters 11/15 Tracking
Performance of Adaptive Filters 11/17 11/22 11/24 Project Presentations 11/29 12/1 Paper Presentations & Discussions 10% Computer Project 15% Homework 15% Midterm Exams 15% each Final Exam 30% * Papers and requested presentation dates must be submitted to the
instructor by e-mail for prior approval. Upon approval, the presentation slides
must be sent to the instructor before the presentation date. * An extra paper presentation for 10% bonus point may be
accommodated. Prior consent from the instructor is required. * These weights are approximate and may be subject to change.
* Late homework will not be accepted. Homework (updated 11-15-2021) HW1: out 9/20 due 9/27 -- Adaptive Filters (textbook), problems I.10, I.11, I.12 HW2: out 9/20 due 9/29 -- Adaptive Filters (textbook), problems II.1, II.5, II.24, II.37 HW3: out 10/21 due 11/03 -- Adaptive Filters (textbook), problems III.1, III.3, VII.6(a), VII.35 HW4: out 11/15 due 12/03 -- Adaptive Filters (textbook), problems IV.2(a)(b)(f), IV.14, IV.15 Paper presentations The presentations should be around 20-25 mins. Some suggested papers
can be found here. Note Students who wish to observe religious holidays should notify the
instructor by the tenth day of the semester of the date when they will be
absent unless the religious holiday is observed on or before the tenth day of
the semester. In such cases, the students should notify the instructor at least
five days in advance of the date when he/she will be absent. Every reasonable
effort will be made to honor the request. Academic dishonesty by students including plagiarism will result
in appropriate disciplinary action. Intentional use or attempt to use
unauthorized assistance, materials, information, or people in any examination,
quiz, or assignment may lead to penalties such as a failing grade. College of
Engineering and University guidelines will be followed.Lectures &
office hours
Textbook and
optional references
Lectures
The slides are available here. The video lectures
and student presentation slides will be available on Google
Drive. The list of Wednesday on-campus events and presentations can be
found here.
Introduction to ECE 516
Basics- A Refresher
Selected Topics (Big Data & Machine Learning)
Project Presentations
Project Presentations
Project Presentations
Final Exam Week 12/6- 12/10
Course requirements
and grading
