ECE 561: Statistical Inference for Engineers and Data Scientists
(aka Detection and Estimation), Spring 2026

• Class time and place: 11:00 - 12:20 TR in Room 2013 ECEB (in-person only)


• Instructor: Prof. V. V. Veeravalli. 315 CSL, vvv at illinois dot edu, 333-0144
  Office Hours: Tuesdays 4PM - 5PM on Zoom. Zoom Coordinates.


• Teaching Assistant: Argy Gerogiannis, ag91 at illinois dot edu
  TA Office Hours: Wednesdays 12PM-1PM on Zoom. Zoom Coordinates.


• The Gradescope entry code is here . Please change your gradescope Student ID to your University UIN.

Announcements

• Exam 1 will be held on Monday, March 9 from 7:00-8:50 PM in ECEB 2013 (your regular classroom). You will be allowed one sheet of handwritten notes (8.5''x11'', both sides) for the exam. Otherwise the exam is closed book.
• Exam 2 will be held on Monday, April 27 from 7:00-8:50 PM in ECEB 2013 (your regular classroom). You will be allowed two sheets of handwritten notes (8.5''x11'', both sides) for the exam. Otherwise the exam is closed book.
• The final exam will be held on Friday, May 8 from 1:00-4:00 PM in ECEB 2013 (your regular classroom). You will be allowed three sheets of handwritten notes (8.5''x11'', both sides) for the exam. Otherwise the exam is closed book.

Homework Assignments and Exams

Note: Please submit your homework assignments on Gradescope. Scan your handwritten solutions (typed solutions are not allowed) into a pdf file to submit. At the time of submission please don't forget indicate which page/s contain the answers to each of the questions. Think about the problems on your own, without consulting online resources; you will get full credit for demonstrating a reasonable effort on each problem even if you end up making some errors. You can discuss the problems with your fellow students if you like, but each of you must write and submit independent solutions.

• Homework Assignment 1: Due Feb 5, 2026, 11 AM on Gradescope. Solutions.
• Homework Assignment 2: Due Feb 19, 2026, 11 AM on Gradescope. Solutions.

Lecture Notes, Videos, Handouts

• Lecture 1 (pre), and Lecture 1 (post).
• Lecture 2 (pre), and Lecture 2 (post).
• Lecture 3 (pre), and Lecture 3 (post).
• Lecture 4 (pre), and Lecture 4 (post)
• Lecture 5 (pre), and Lecture 5 (post).
• Lecture 6 (pre), and Lecture 6 (post).
• Lecture 7 (pre), and Lecture 7 (post).
• Lecture 8 (pre), and Lecture 8 (post).
• Lecture 9 (pre), and Lecture 9 (post).
• Lecture 10 (pre), and Lecture 10 (post).

Course Details

• Syllabus: Statistical inference for engineers and data scientists: fundamental principles of statistical decision theory and their application to hypothesis testing and estimation; classical optimality criteria for decision rules; computationally efficient implementations; sequential decision-making; performance analysis; asymptotic properties and performance of decision rules. Prerequisite: ECE 534.

  Textbook: P. Moulin and V.V. Veeravalli. Statistical Inference for Engineers and Data Scientists. Cambridge University Press, 2019. Errata list .

  References reserved in Grainger Library:
  621.3822 L579p Levy, B.; Principles of Signal Detection and Parameter Estimation
  621.38043P791I1994 Poor, H. V.; Introduction to Signal Detection and Estimation 2nd ed.
  621.38153V36d - Vol. 1 Van Trees, H. L.; Detection, Estimation, and Modulation Theory
  621.3822K46f Kay, Steven M.; Fundamentals of Statistical Signal Processing: Estimation Theory v.2
  621.3822k46fv.1 Kay, Steven; Fundamentals of Statistical Signal Processing v.1
  519.5l52t1997 Lehmann, E.L.; Testing Statistical Hypotheses 2nd ed.
  620W84S1985 Wong, E./Hajek, B.; Stochastic Processes in Engineering Systems

  Exams, homework, grading, etc.:

• Homework (H) problems will be assigned on a (approx.) fortnightly basis and be submitted via Gradescope . No late homework will be accepted (unless an extension is granted in advance by the instructor).
• There will be two mid-term exams and one final exam. The first midterm exam (M1) will be held in the beginning of March, and the second midterm exam (M2) at the end of April. The final exam (F) will be held on May 8.
  
• Your course grade will be determined by your performance in H, M1, M2 and F, according to the formula:
  
  Score = .1H + max{.35M1 + .25M2 +.4F, .25M1 + .35M2 + .4F, .25M1+.25M2+.5F},
  
  where H, M1, M2 and F are normalized to 100.