Probability and information theory (5 cr)

Objective

You know the basic concepts of probability and information theory. You are able to make decisions by means of random variables and probability distributions. You are able to apply information theory in the field of information technology.

Content

What do sample space and random variable mean and how is probability defined?
Which kind of predefined distributions are there for random variables?
What is meant by information theory?
What is meant by maximum entropy principle?
How are probability and information theory applied in information technology and decision-making?

Course material

To be settled at the course kick off. There will be book chapters, articles and other reading materials in Learn, provided by the teacher.

Study forms and methods

Scheduled track:
You participate in lectures and exercises according to the weekly schedule.

RDI and work-related cooperation

-

Timing of exams and assignments

There are two exams: the midterm and final exams. See the assessment criteria for details on grading.

Student workload

135h of student work.

Course part description

This course is designed to give students a solid foundation in probability theory, statistics, and information theory, with an emphasis on how these areas interconnect. Students will learn to model uncertainty, analyze data, and understand how information is quantified, compressed, and transmitted.

Course Outcomes:
By the end of the course, students will:

Have a foundational understanding of probability, statistics, and information theory.
Be able to model uncertainty, analyze data, and understand the basic principles of data compression and communication.
Be able to apply these concepts to real-world problems in fields such as data science, telecommunications, and IT.

Evaluation scale

1-5

Assessment methods and criteria

The course grade is 1-5.

Assessment:
- Homework Assignments (40%): Weekly assignments covering probability, statistics, and information theory.
- Midterm Exam (20%): Assessment of probability and introductory statistics concepts.
- Final Exam (30%): Comprehensive exam covering all topics, with emphasis on connections between probability, statistics, and information theory.
- Final Project (10%): A small case study where students choose a real-world problem involving probability, statistics, or information theory (e.g., analyzing data from a survey, simulating a communication system).

Qualifications

Attending the course requires basic knowledge of sets, Boolean algebras and calculus.