Topic outline

  • General

    Course title: ELEC-C5310 Introduction to Estimation, Detection and Learning

    Credits: 5 cr.

    Teachers: Profs. Sergiy A. Vorobyov, Esa Ollila, and  Visa Koivunen 

    Prerequisites: Basics on Probability; Matrix Calculus, Signals and Systems

    Time and Location:  

    Lectures: Wednesdays 13:15-16:00 (videos and notes are available for self-study, and we meet on Weds. for answering your questions and having discussions)

    Exercise: Thursdays 12:15-14:00 (initially takes place in zoom and may be moved in class later)

    Hybrid Teaching (give you access to recordings and then we meet in class or zoom (by agreement) to discuss. Problem solving sessions will be in class or zoom (by agreement). 

    Homework Assignments: There will be 6-7 homework assignments, one roughly every 10 days

    Exam Time and Location: we reserve an option to have a light exam 

    Grading: All components of the class give approximately the same percentage for the final grade (Example: with 7 homework assignments and no final exam, each assignment will give on average 14.3% for the final grade - in fact, some initial assignments will give 14% and some later assignments - 15%)

    Motivation: In every digital system that measures, generates, transmits, processes, and uses data, i.e., in every digital system that you will possibly deal with independent on a specific application, you will need to

    1. Estimate signals and parameters of interest,
    2. Detect events from data,
    3. Learn and infer from data.

    This course will introduce you to the main principles and approaches for addressing such tasks.

    Textbook: Probability and Stochastic Processes – A Friendly Introduction for Electrical and Computer Engineers, Roy D. Yates and David J. Goodman, John Wiley & Sons, Inc., 2nd Ed.

    Learning Outcomes: 

    1. Develop understanding and skills for using the tools of probability, signal and systems and learning theory to estimate signals and parameters of interest, to detect events from data, and to learn from data.

    2. Starting from the univariate case, develop understanding of multivariate analysis as well. Develop understanding how to identify the optimal estimator/detector or at least bound the performance of any estimator/detector.

    3. Develop understanding and skills for using detection approaches (statistical hypotheses testing): power of the test, Neyman-Pearson test, likelihood ratio test, matched filter detection, sequential test. Understanding radar detection. 

    4. Basic estimation concepts and the method of maximum likelihood, basic concepts of supervised learning and methods such as linear and quadratic classification. 

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      Folder icon
      Exercises Folder

      Solutions to exercise on 20.01.2022 are Available here!

      Solutions to exercise on 27.01.2022 are Available here!

      Solutions to exercise on 10.02.2022 are your notes from class!

      Solutions to exercise on 17.02.2022 are Available here! (was a long one)

    • File icon
      Wiener Filter and LMS adaptive filter handout File PDF