ELEC 631 - Advanced Digital Signal Processing: Graphs and Signal Processing - Spring 2017


This course will explore two directions related to graph models for signal processing.

First, we will study graph signal processing, which merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process signals on graphs. We will learn how to leverage the structure communicated by graphs in order to process information effectively.

Second, we will study graph based statistical models for signals, i.e., probabilistic graphical models. These elegantly use the combinatorial properties of the graph to encode statistical information about signals. We will both understand how to perform efficient statistical signal processing (inference) given such graph structures and understand how to learn these graph structures from the signals themselves.

Applications abound and include social, energy, transportation, sensor, and neural networks; speech and image processing; classical and deep machine learning.

Topics to be discussed include: Algebraic and spectral graph theory, signals on graphs, inference and learning in probabilistic graphical models, high dimensional statistics.


Instructors:

Gautam Dasarathy
2017 A, gautamd@rice.edu
Office hours: By appointment
Richard Baraniuk
Duncan Hall 2028, 713-348-5132, richb@rice.edu
Office hours: By appointment

Teaching Assistant:

Tan Nguyen
832-614-0285, mn15@rice.edu

Location: Duncan Hall 1075

Time: Friday, 2 - 4:30 PM

Prerequisites: ELEC 531, ELEC 533 (suggested)

The course is open to graduate students from any department with some background in statistics or machine learning.

Credits: 3

Course Goals and Objectives
This is a "reading course", meaning that students will select, read and present classic and recent papers from technical literature to the rest of the class in a lively debate format. Discussions aim at identifying common themes and important trends in the field. Students will also get hands on experience with graph signal processing software and complete a major group project.

Course Outcomes
Deep understanding of graph signal processing.

Class Participation
Students are expected to attend, present papers, prepare a summary each week and complete a major group project.

Grading

  • Class participation (15%)
  • Paper presentations (30%)
  • Paper summaries (prepared each week, 15%)
  • Group project (40%)

Schedule (Tentative)

  • 13 January 2016: Orientation
  • 20 January 2016: Student Paper Presentations Begin
  • Early May 2016: Group Project Presentations

Discussion Forum
Please follow the link below to register for ELEC 631 on Piazza. We will use Piazza as our discussion forum in this course.
https://piazza.com/rice

Honor Code Policy:
General Assignments:
Complete homework assignments individually. You may freely use course notes or papers presented for the assignments. You may discuss and compare ideas on the assignments, but each student must write up solutions individually without copying. Clearly state any assumptions that you make in order to solve the problems and show all your work.

Exam Honor Code:
Complete all exams individually. You may not work with others.

Students with Disabilities:
Any student with a disability requiring accommodations in this course is encouraged to contact the instructor after class or during office hours. Additionally, students will need to contact Disability Support Services in the Ley Student Center.

Updates to the Course:
Information contained in this course syllabus, other than the absence policies, may be subject to change with reasonable advance notice as appropriate.

Debate Format:
Each week, two teams of two students each will discuss that week’s assigned paper(s). The first presentation will be approximately 1 hour from the “Defense” team, who will present the paper proudly as though it was their PhD thesis. The second presentation will be approximately 30 minutes from the “Offense” team, who will attack the paper using any and all means available to them (the literature, follow-on papers, Wikileaks, etc.). Students not presenting are expected to have read the paper(s) and jump in on the debate.

Paper Assignments

  • TBA

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