Course syllabus

Jump to today

ESS013 Sannolikhetsteori och statistisk signalbehandling LP4 VT25 (7,5 hp)

The course is offered by the Dept. of Electrical Engineering. The formal course plan is found at the Student Portal. 

Info for Students who have followed ESS012

The following rules apply only for students who have been registered on ESS012. 

Students that have

  • Passed the ESS012 project but not the exam
    • Pass the ESS012 re-exam (offered at the same date and time as the ESS013 exams, both first exam and re-exams)
    • It is possible to take the ESS013 quiz and programming assignments to improve scores
    • The ESS012 project cannot be reported as an approved ESS013 project (due to differences in the projects)
    • Students registered for ESS013 should de-register from the course. In case Canvas access is revoked, email Erik Ström erik.strom@chalmers.se for renewed access
  • Passed the ESS012 exam but not the project
    • Take ESS013, i.e., register for the course
    • Cancel ESS012 (lägg avbrott)
    • The ESS012 exam can be credited towards ESS013
  • Has not passed ESS012 project or exam
    • Take ESS013, i.e., register for the course and complete all requirements
    • Cancel ESS012 (lägg avbrott) 

As needed, please contact a student counselor (studievägledare) for advice.

Language

The official language of the course is Swedish. Nevertheless, written course material (text book, slides, assignments, etc.) will be in English for several reasons, including preparing students for an engineering career, which today requires a good command of English and knowledge of English terminology. Spoken language at lectures, exercises, etc. will be Swedish, except when the teacher is not fluent in Swedish.

Contact

Student course representatives are

Communication with teachers can be via email (preferred) or via Canvas. Please mark emails with [ESS013] in the subject heading.

Questions about quizzes, programming assignments, project and exercises are directed to Kaan. Questions about lectures or course organization, grades, or other general matters are directed to Erik. 

Examiner and lecturer:

Guest lecturer

Teaching assistant:

Aims and objectives

The course aims to provide basic knowledge of probability and mathematical statistics, primarily from an application point of view, and to give an understanding of the underlying models and approaches. Special emphasis is put on interpretation and modeling such that the course theory will be useful for future courses and to solve engineering problems in practice. Connections to problems in modern statistical signal processing, e.g., digital communication, machine learning and artificial intelligence will be provided.

Schedule

The schedule is, of course, available in TimeEdit. Lectures and exercises content and deadlines for quizzes, programming assignments, and project are found in the Week Plan.

Text books

Yates and Goodman is the main text, and students are strongly recommended to acquire it. It is an excellent book that will also be useful as a reference in future courses. In addition, students are allowed to bring the book to the exam.

The James, Witten, Hastie, and Tibshirani book will be used to complement Yates and Goodman on material regarding linear regression. It is also an excellent book, but perhaps buying it would not be recommended as a new edition is coming. Finally, the book by Gillard is used to complement Yates and Goodman on material regarding confidence intervals. It is an easy-to-read book that can also be used to give a second presentation on the material in the other books.

Solution manual to Yates and Goodman is available for download in the Files section.

Course organization

The course consists of scheduled lectures and exercises during which the theory is discussed and problems are solved. Reading instructions for lectures and references to problems for exercises are found in the Week Plan.

Students are expected to complete six quizzes, six programming assignments (in MATLAB), and one project. Deadlines for these are found in the Week Plan. The quizzes, programming assignments, and projects are found in the Assignments section. Hand-ins are graded, and scores are counted towards the final grade according to the rules below.

Deadlines are strict. No late hand-ins will be graded, and no extensions will be granted. There will be no possibility of complementing a hand-in after the submission deadline.

Changes since last year

  • Lecture material has been redistributed
  • Programming assignments have been revised
  • Project description has been revised
  • The syllabus (this page) has been updated

Learning outcomes

After completing the course, the student should be able to (in Swedish, which is the official text)
  • förklara fundamentala begrepp inom sannolikhetsläran, tex. utfallsrum, händelser, sannolikhetsmått, betingad sannolikhet, oberoende, stokastiska variabler, fördelnings- och täthetsfunktion, samt använda dessa begrepp för att lösa problem
  • redogöra för skillnaden mellan en frekventistisk och en Bayesiansk ansats
  • använda Bayes formel, stora talens lag, samt centrala gränsvärdessatsen i problemlösning
  • utföra punktskattning och hypotestest samt uppskatta kvalitén i dessa
  • konstruera Matlab-program för att lösa problem inom statistisk signalbehandling

Learning outcomes in English translation: the student should be able to

  • explain fundamental concepts in probability theory, e.g., sample space, events, probability measure, conditional probability, independence, random variables, probability density function, cumulative density function, and be able to use these concepts to solve problems
  • explain the difference between the frequentist and Bayesian approaches
  • use Bayes theorem, the law of large numbers, and the central limit theorem to solve problems
  • perform point estimation and hypothesis testing, and to estimate the quality in these 
  • design MATLAB-programs to solve statistical signal processing problems 

Examination

The course has two examination modules

  • Project: 2 hp, pass/fail grade 
  • Exam: 5.5 hp; U, 3, 4, 5 grade

To pass the course, students need to pass both modules.

Project Module Grading Rules

The purpose of the project is to facilitate learning of the course material. All team members should contribute equally towards accomplishing the project tasks. Even if the work is split up between group members, the group members are collectively responsible for the results and should be able to answer detailed questions about any part of the project at the project oral exam. Writing code from scratch is an efficient way to learn, and it is therefore not allowed to copy code from Internet or other sources or to use AI-tools from programming.

General rules

  • AI tools like ChatGPT can be used to assist in writing the report, but not for programming
  • Group members are fully responsible for the correctness of the text, regardless of if AI tools are used or not
  • Copying code from the Internet or other sources or using AI tools such as ChatGPT is not allowed
  • Cooperation between groups is not allowed

Violating the above rules will be considered cheating and is subject to disciplinary action. 

Grading rules 

  • Report:
    • The report should
      • document the project (i.e., explain what was done, why it was done, and what the results were)
      • be concise, easy to follow, and written in good English
      • follow standard practices for technical reports, see Chalmers Writing Guide for advice
      • contain a section that clearly states the contribution of each group member, including report writing and programming
      • contain an appendix with carefully commented MATLAB code used to solve the project
      • results should be commented on and checked to be reasonable and consistent with theor
      • plots must be clearly labeled with units
    • Late reports will not be graded, see Week Plan for submission deadline
    • The maximum report score is 6 points and the minimum passing score is 3 points.
    • Report scores are common, i.e., all group members receive the same score
  • Oral exam:
    • All group members should be able to explain and defend all details about the project report at the oral exam.
      • For example, suppose Student A has programmed a MATLAB function; then Student B and C should be able to explain the code at the oral exam
    • Only groups that have submitted the report before the deadline can take the oral exam
    • The maximum oral exam score is 2 points and the minimum passing score is 1 point
    • Oral exam scores are individual, i.e., group members may receive different scores
  • To pass the project, a student must receive at least 3 points on the report and at least 1 point on the oral exam
    • Students who fail the oral exam can request one extra oral exam
      • A student can get at most 1 point for the extra oral exam
    • Students who fail the report can request to hand in an improved report (before a specific deadline)
      • A student group can get at most 3 points for the improved report

Students with failing project grades must pass the project the next academic year.

Written Exam Module Grading Rules

Exam grading policy: An erroneous answer or incomplete or poorly motivated solutions give point reductions down to a minimum of 0 points.  As a general rule, bad motivation or errors related to the course's fundamental principles will lead to larger point reductions. Computational errors that do not lead to unreasonable answers generally give smaller reductions.

Assuming that the exam will take place on campus, the allowed material

  • Chalmers-approved calculator
  • L. Råde, B. Westergren. Beta, Mathematics Handbook, any edition. 
  • Roy D. Yates, and David J. Goodman, Probability and Stochastic Processes, 3rd Edition, International student version, Wiley 2015.
  • Notes are not allowed in the books above, except for
    • Correction of errors in the books
    • Highlights or underlining
    • Stickers for quick indexing and finding of material in the books (including labeling of the stickers)
  • One A4 page with your own handwritten notes. Both sides of the page can be used. Photocopies, printouts, other students' notes, or other material are prohibited.
  • A paper-based dictionary without added notes (electronic dictionaries are not allowed).

See the Week Plan for the likely date for the exam. Please check LADOK for definitive information about date and time. 

Summary of Point Distribution

Quizzes, programming assignments, and the project are graded and counted toward the final grade. Quizzes and programming assignments are not mandatory but highly recommended.

The maximum scores per quiz, programming assignment, project, and written exam are defined in the table below.

What Instances Max points per instance Max points Min points for passing grade
Quiz  6 3 18 0
Programming assignment 6 3 18 0
Project report 1 6 6 3
Project oral exam 1 2 2 1
Written exam* 1 56 56 20
Total 100  (see below for grades)

*) note that to pass the course module "Exam" and receive 5.5 hp, the total course score must be at least 45 points, of which the written exam score is at least 20 points. 

Points for quizzes, programming assignments, and the project do not expire. That is, they are kept indefinitely until the student passes the course.

Final Grades

To pass the course, a student must have 

  • acquired at least 1 point on the project oral exam
  • acquired at least 3 points on the project report
  • acquired at least 20 points on the written exam
  • acquired at least 45 points from the written exam, quizzes, programming assignments, and project

The final grade is set according to the following table.

Stot = Total score (quizzes, programming assignments, project, exam) Exam score Grade
Stot < 45 ≥20 U
Stot LaTeX: \in [45, 65)  ≥20 3
Stot LaTeX: \in [65, 80) ≥20 4
Stot LaTeX: \in [80, 100] ≥20 5

Note that quizzes and programming scores are counted towards the final grade. It is highly recommended to complete these. A relatively high exam score is otherwise needed to reach a passing grade.

 

Course summary:

Date Details Due