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, Zoom

    Exercise: Thursdays 12:15-14:00, Zoom

    Exam Time and Location: April 7, 13:15-16:00, Zoom

    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.

    Course Outline/Schedule:

    Course Oultline/Schedule

    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. Practicing basic concepts such as: sufficient statistics, bias and mean squared error, maximum likelihood, Bayesian estimation and learning.