STK4141 – Probabilistic graphical models
Course description
Schedule, syllabus and examination date
Course content
The course provides an introduction to probabilistic graphical models, which makes up one of the most important frameworks for modeling systems over many variables that interact with each other. The field of graphical models lie at the intersection of statistics and computer science, combining concepts and methods from statistics, probability theory, graph algorithms and machine learning. The course will focus on the two most central model classes: Bayesian networks and Markov networks, and it will cover the main topics related to representation, inference, and learning.
Learning outcome
After completing the course you
- know how conditional independence can be exploited to model high-dimensional joint distributions
- know how to interpret graphs as representations of complex dependence structures
- understand how the structure of a graphical model can be exploited to answer probabilistic queries in an efficient way
- know about methods for learning graphical models from data
- have been introduced to causal graphical models and how they can be used to answer causal queries
- will be familiar with R packages for performing inference in and learning of graphical models.
Admission to the course
Students admitted at UiO must?apply for courses?in Studentweb. Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.
Nordic citizens and applicants residing in the Nordic countries may?apply to take this course as a single course student.
If you are not already enrolled as a student at UiO, please see our information about?admission requirements and procedures for international applicants.
Recommended previous knowledge
- Probability and probability models equivalent to STK1100 – Probability and Statistical Modelling
- Statistical methods equivalent to STK1110 – Statistical Methods and Data Analysis
- Basic programming skills equivalent to IN1900 – Introduction to Programming with Scientific Applications
Overlapping courses
- 10 credits overlap with STK9141 – Probabilistic graphical models.
Teaching
4 hours of lecture/exercises per week throughout the semester.
The course may be taught in Norwegian if the lecturer and all students at the first lecture agree to it.
Upon the attendance of three or fewer students, the lecturer may, in conjunction with the Head of Teaching, change the course to self-study with supervision.
Examination
Final written exam or final oral exam, which counts 100 % towards the final grade.
The form of examination will be announced by the lecturer by 1 October/1 March for the autumn semester and the spring semester respectively.
This course has 1 mandatory assignment that must be approved before you can sit the final exam.
It will also be counted as one of the three attempts to sit the exam for this course, if you sit the exam for one of the following courses: STK9141 – Probabilistic graphical models
Examination support material
Written examination: Approved calculators are allowed.?Information about approved calculators in Norwegian.
Oral examination: No examination support material is allowed.
Language of examination
Courses taught in English will only offer the exam paper in English. You may write your examination paper in Norwegian, Swedish, Danish or English.
Grading scale
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Resit an examination
This course offers both postponed and resit of examination. Read more:
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.