FY835: Information theory, inference, and learning algorithms
Study Board of Science
Teaching language: Danish, but English if international students are enrolled
EKA: N510046112, N510046102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Spring
Level: Master
STADS ID (UVA): N510046101
ECTS value: 5
Date of Approval: 06-11-2023
Duration: 1 semester
Version: Approved - active
Entry requirements
Academic preconditions
Students taking the course are expected to: Have knowledge of probability theory, (e.g. conditional probabilities).
Course introduction
The course aims to give the student an introduction to information theory in particular, but also Bayesian inference and neural networks, and to show how these topics are different sides of the same coin.
Information theory plays a major role in statistical physics, as the mathematical theory of entropy, and inference and machine learning are central to for instance analysis of experimental data. The course can therefore be applied in projects during the rest of the education.
In relation to the competence profile of the degree the explicit focus of the course is on:
- research-based knowledge of the basic theory formations and experimental methods of physics.
- describing, formulating and communicating issues and results to peers as well as non-specialists, partners and users.
Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:
- Autonomously acquire the knowledge about information theory, inference and machine learning as described under Contents
- Present theory and exercises
- Apply the acquired knowledge within a chosen subject (final project)
Content
The following main topics are contained in the course:
- Interpretation of probability
- Model selection and parameter fitting
- Shannon entropy
- Data compression
- Communication over noisy channels
- Monte Carlo methods
- Neural networks
Literature
Examination regulations
Prerequisites for participating in the exam a)
Timing
Spring
Tests
Active participation in the teaching
EKA
N510046112
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
0
Additional information
The prerequisite examination is a prerequisite for participation in exam element a).
Active participation in class. Specifically one presentation or solved exercise for each meeting with the teacher.
Active participation in class. Specifically one presentation or solved exercise for each meeting with the teacher.
Exam element a)
Timing
June
Prerequisites
| Type | Prerequisite name | Prerequisite course |
|---|---|---|
| Examination part | Prerequisites for participating in the exam a) | N510046101, FY835: Information theory, inference, and learning algorithms |
Tests
Oral exam
EKA
N510046102
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
5
Additional information
The exam takes its starting point in a written report about the final project. It takes the form of a presentation followed by questions.
Indicative number of lessons
Teaching Method
At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.
- Intro phase (video lectures): approximately 16 hours
- Training phase: 30 hours, in the form of meetings with the teacher.
The teaching is mainly structured around a series of video lectures, study of the textbook and the solving of exercises. Once a week there is a meeting with the teacher, where video lectures and textbook material are discussed and assignments are reviewed.
Activities during the study phase:
- Study of the textbook
- Solving of exercises
- Preparation of presentations
- Carrying out the final project
Teacher responsible
Timetable
Administrative Unit
Team at Educational Law & Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.