ECE 561: Statistical Inference for Engineers and Data Scientists, Spring 2024

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 5PM - 6PM on Zoom. Zoom Coordinates.

Teaching Assistant: Adarsh Subramaniam, adarshm2 at illinois dot edu
Office hours: Wednesdays 4PM-5PM. Venue: ECEB 3036, Zoom: Zoom Coordinates.


Please add yourself to the Piazza discussion site for the course.

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

Announcements

• There will be no class on Thursday, April 18.
• There will be no class on Tuesday, Feb 27.
• Exam 1 will be held on Tuesday, March 5 from 7-8:30 PM in ECEB 3013 (note that this is different from 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 Tuesday, April 23 from 7-8:30 PM in ECEB 3013 (note that this is different from 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.

Homework Assignments and Exams

Note: Please submit your homework assignments on Gradescope. Scan your handwritten solutions 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.

• Homework Assignment 1: Due Feb 1, 2024, 11 AM on Gradescope. Solutions.
• Homework Assignment 2: Due Feb 15, 2024, 11 AM on Gradescope.(Your solutions need to be handwritten, not typed.) Solutions.
• Homework Assignment 3: Due Feb 29, 2024, 11 AM on Gradescope.(Your solutions need to be handwritten, not typed.) Solutions.
• Exam 1 Spring 2021. Solutions.
• Homework Assignment 4: Due March 25, 2024, 11 AM on Gradescope.(Your solutions need to be handwritten, not typed.) Solutions.
• Project Guidelines Abstract due April 2 on Gradescope.
• Homework Assignment 5: Due April 4, 2024, 11 AM on Gradescope.(Your solutions need to be handwritten, not typed.) Solutions.
• Homework Assignment 6: Due April 18, 2024, 11 AM on Gradescope.(Your solutions need to be handwritten, not typed.) Solutions.
• Project Presentation and Report Guidelines

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), and Lecture 4 pre-recorded video
• 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).
• Lecture 11 (pre), and Lecture 11 (post).
• Lecture 12 (pre), and Lecture 12 (post).
• Lecture 13 (pre), and Lecture 13 (post).
• Lecture 14 (pre), and Lecture 14 (post).
• Lecture 15 (pre), and Lecture 15 (post).
• Lecture 16 (pre), and Lecture 16 (post).
• Lecture 17 (pre), and Lecture 17 (post).
• Lecture 18 (pre), and Lecture 18 (post).
• Lecture 19 (pre), and Lecture 19 (post).
• Lecture 20 (pre), and Lecture 20 (post).
• Lecture 21 (pre), and Lecture 21 (post).
• Lecture 22 (pre), and Lecture 22 (post).
• Lecture 23 (pre), and Lecture 23 (post).
• Lecture 24 (pre), and Lecture 24 (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 . Collaboration on the homework is permitted, however each student must write and submit independent solutions. 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 paper review project. The first midterm exam (M1) will be held in the beginning of March, and the second midterm exam (M2) at the end of April. Details on the paper review project (R) will be provided later.
  
• Your course grade will be determined by your performance in H, M1, M2 and P, according to the formula:
  
  Score = .2H + .3R + max{.3M1 + .2M2, .2M1 + .3M2},
  
  where H, M1, M2 and R are normalized to 100.