The course consists of weekly lectures and 4 help/lab sessions distributed over the teaching period. Students will complete 4 Python-based programming assignments, designed to reinforce theoretical concepts through practical implementation. Participation in help sessions is encouraged, as they provide guided support for the assignments.
The course runs over 8 weeks and does not include a written exam; assessment is based entirely on the assignments.
DD2447 Statistical Methods in Applied Computer Science 6.0 credits
This course provides a deep dive into probabilistic machine learning, focusing on modeling uncertainty, reasoning under incomplete information, and learning from data using statistical methods. Based on Kevin Murphy’s book “Machine Learning: A Probabilistic Perspective,” the course is designed for students who want an in-depth and modern foundation in machine learning.
Through a combination of theory and hands-on exercises, students will develop the skills to build interpretable and flexible models that go beyond black-box approaches. This course is ideal for those interested in research, data science, and advanced AI applications at the second- and third-cycle levels.
Information per course offering
Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus DD2447 (Autumn 2022–)Headings with content from the Course syllabus DD2447 (Autumn 2022–) are denoted with an asterisk ( )
Content and learning outcomes
Course disposition
Course contents
- Generative models.
- Bayesian inference.
- Probabilistic programming.
- Graphical models.
- Concealed Markov models with continuous states.
- Particle filters.
- Monte Carlo estimation.
- Sequential Monte Carlo.
- Markov Chain Monte Carlo.
- Clustering.
- The Dirichlet process.
Intended learning outcomes
After passing the course, the student shall be able to
- explain and justify several important methods for machine learning
- give an account of several types of methods and algorithms that are used in the field of sample-based inference methods
- implement several types of methods and algorithms that are used in the field based on a high-level description
- extend and modify the methods that the course deals with
in order to be able to make a degree project in sample-based inference methods.
Literature and preparations
Specific prerequisites
Knowledge in algebra and geometry, 7.5 higher education credits, equivalent to completed course SF1624.
Knowledge in one variable calculus, 7.5 higher education credits, equivalent to completed course SF1625.
Knowledge in probability theory and statistics, 6 higher education credits, equivalent to completed course SF1910-SF1924/SF1935.
Knowledge and skills in programming, 6 higher education credits, equivalent to completed course DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1321/DD1331/DD1337/DD100N/ID1018.
Knowledge in algorithms and data structures, at least 6 higher education credits, equivalent to completed course DD1320/DD1321/DD1325/DD1326/DD1327/DD2325/ID1020/ID1021.
Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course.
Being registered for a course counts as active participation.
The term 'final examination' encompasses both the regular examination and the first re-examination.
Recommended prerequisites
Courses in mathematics (analysis), programming, computer science and statistics equivalent to obligatory courses on D- or F-programme.
Matlab or similar tool (Octave, R).
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
A, B, C, D, E, FX, F
Examination
- INL1 - Assignment, 6.0 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
Other requirements for final grade
Written assignments and a project (INL1; 6 higher education credits).
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Computer Science and Engineering
Education cycle
Second cycle
Supplementary information
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex
This course overlaps with DD2420 Probabilistic Graphical Model.