Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

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. 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. Develop understanding and skills for using detection approaches (statistical hypotheses testing). Understanding radar detection. Practicing basic concepts such as: sufficient statistics, bias and mean squared error, maximum likelihood, Bayesian estimation and learning.

Credits: 5

Schedule: 05.09.2023 - 28.11.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Sergiy Vorobyov, Esa Ollila, Visa Koivunen

Contact information for the course (applies in this implementation):

Via Mycourse or personal emails/phones of the lecturers and teaching assistant. 

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content
  • valid for whole curriculum period:

    Statistical modeling and basic distributions. Parameter estimation. Hypothesis testing detection theory. Basics of learning theory.

  • applies in this implementation

    Credits: 5 cr.

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

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

    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.

    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): 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. 


Assessment Methods and Criteria
  • valid for whole curriculum period:

    Lectures, exercises, assignments, final exam.

  • applies in this implementation

    Grading: All components of the class give approximately the same percentage for the final grade (Example: with 6 homework assignments and no final exam, each assignment will give on average 16-17% for the final grade - in fact, some initial assignments will give 16% and some later assignments - 17%)

    Homework Assignments: It is expected that there will be 6 homework assignments, but we will have to plan it more carefully by talking to you and optimizing the schedule. The plan is to have lighter assignments more frequently!

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


Workload
  • valid for whole curriculum period:

    Lectures, exercises, assignments, final exam approximately 50 h, independent work approximately 83 h, total 133 h

    Attendance in some contact teaching may be compulsory.

  • applies in this implementation

    The work load consists of in class work (lectures and exercise sessions) and home works for solving problems from the assignments at home. 

DETAILS

Study Material
  • applies in this implementation

    Lecture slides!!! It is necessary to come to every lecture.


    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.

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 No teaching
    2023-2024 Autumn I - II

Details on the schedule
  • applies in this implementation

    Time and Location:  

    Lectures: Tuesdays 9:15-12:00 (AS6, Maarintie 8)

    Exercise: Mondays 9:15-12:00 (check the table for the exact days, not every Monday)