6 ECTS credits
168 h study time

Offer 1 with catalog number 1024251ANR for all students in the 1st semester at a (A) Bachelor - preliminary level.

Semester
1st semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Enrollment Requirements
Students must have followed Machine Learning, Introduction to Artificial Intelligence, Mathematics: Calculus and Linear Algebra, Probability Theory and Statistics, Algorithms and Data Structures 1,Structure of Computer Programs 1, Logic and Formal Systems, Discrete Mathematics, AI Programming Project before they can enroll for Bayesian Methods
Taught in
Dutch
Faculty
Faculty of Sciences and Bioengineering Sciences
Department
Computer Science
Educational team
Pieter Libin (course titular)
Activities and contact hours
24 contact hours Lecture
24 contact hours Seminar, Exercises or Practicals
12 contact hours Independent or External Form of Study
Course Content

This course covers Bayesian methods, or data-based methods that directly perform statistical analysis of the data. 

These techniques use Bayes' theorem to combine a priori knowledge with observations (i.e., data points) from reality. 

Examples of techniques are: Bayesian Networks, Bayesian inference and Markov Chain Monte Carlo.

 

Additional info

NA

Learning Outcomes

General competencies

  • In-depth knowledge and understanding of Bayesian statistics, inference and machine learning.
  • Be able to formulate, apply, implement and validate Bayesian models.
  • Be able to create a project plan to solve a typical learning problem with Bayesian techniques.
  • Be able to independently update the knowledge acquired and to tackle new problems in science and applications.
  • Be able to critically search and process academic literature.

Grading

The final grade is composed based on the following categories:
Practical Exam determines 50% of the final mark.
Other Exam determines 50% of the final mark.

Within the Practical Exam category, the following assignments need to be completed:

  • practic. ex. with a relative weight of 1 which comprises 50% of the final mark.

    Note: Project work leading to a paper of maximum 5 pages

Within the Other Exam category, the following assignments need to be completed:

  • other exam with a relative weight of 1 which comprises 50% of the final mark.

    Note: Exam with exercises (written) and theory (oral)

Additional info regarding evaluation

NA

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Bachelor of Artificial Intelligence: Default track (only offered in Dutch)