NMAB21009U Models for Complex Systems (ModComp)
The course is an introduction to probability models capable of capturing dependence structures among many observables. The kind of models treated are often called generative models because they directly provide a data generating mechanism.
Graphical methods play a central role in the course. Graphs provide a natural depiction of dependence but have also a formal mathematical content and are decisive for developing efficient algorithms.
The theoretical part of the course will be illustrated by a number of concrete applications using data, and a practical application of the theory will be part of the compulsory group project.
The following topics will be covered in the course:
- Bayesian networks
- Linear Gaussian networks
- Models with latent variables
- Hidden Markov models
- Gaussian processes
Knowledge
- Graphical representations of dependence and conditional independence
- Standard probability propagation algorithms in a network
- Standard examples of Bayesian networks
- Gaussian models
Skills
By the end of the course, the student must
- master the graph terminology
- master the relation between graphs and probability models
- be able to decide conditional independence by d-separation
- be able to implement simulations of variables from a Bayesian network
- master computations with linear Gaussian networks based on linear algebra
- master computations with discrete networks
- be able to implement ordinary probability propagation algorithms within the framework of Bayesian networks
- be able to implement selected learning algorithms and be able to apply them
Competences
By the end of the course, the student must
- be able to decide correctness and relevans of algorithms as well as theoretical computations within the framework of Bayesian networks
- be able to assess if a Bayesian network correctly represents a specific application
- be able to assess and discuss the benefits and deficits of an algorithm for a specific Bayesian network, e.g. in terms of run time complexity or generality
- be able to solve a larger assignment, that includes theoretical as well as practical elements, in collaboration with others
Will be announced on Absalon
- Category
- Hours
- Lectures
- 28
- Preparation
- 86
- Theory exercises
- 28
- Project work
- 60
- Exam
- 4
- Total
- 206
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentOral examination, 15 minThe continuous evaluation consists of 3 individual quizzes of one hour each, which will be taken as part of the teaching.
The oral exam will consist of a presentation of a select part of the group report and a subsequent discussion. The oral exam is individual and without preparation.
For the final grade each quiz will count by 1/6 and the oral exam will count by 1/2.
The group report as well as the combined assessment of quizzes can be reused for the reexam the same year and the ordinary exam the year after. - Exam registration requirements
The students must write and hand in a report in groups of 4-6 individuals. The report will form the basis for the oral exam, and the report must be handed in to participate in the oral exam.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Re-exam
The reexam is as the ordinary exam.
If the student did not hand in a group report, they must hand in a report before the reexam. If necessary, the report can be made on a individual basis.
The student has the possibility to reuse the group report from the ordinary exam as the basis for the oral reexam, but they are also allowed to hand in a new report, which if necessary can be made on an individual basis.
The student has the possibility to reuse the combined assesment of the quizzes taken during the teaching, but they also has the opportunity to retake all quizzes. These will then have to be taken as one combined, three-hour quiz. It is only possible to retake all three quizzes, in which case the final grade will be based on the assessment of the new quizzes.
Criteria for exam assesment
See the learning outcomes
Course information
- Language
- English
- Course code
- NMAB21009U
- Credit
- 7,5 ECTS
- Level
- Bachelor
- Duration
- 1 block
- Placement
- Block 3
- Schedule
- B
- Course capacity
- No limit
The number of seats may be reduced in the late registration period - Course is also available as continuing and professional education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Sebastian Weichwald (10-7d816f736d72816b766e4a776b7e7238757f386e75)