
This course summarizes statistical and probabilistic methods used in applied Computer Science.
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Content and learning outcomes
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 should 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.
Course Disposition
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Literature and preparations
Specific prerequisites
For non-program students, 90 credits are required, of which 45 credits have to be within mathematics or information technology.
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).
Equipment
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Literature
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Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
A, B, C, D, E, FX, F
Examination
- INL1 - Assignment, 6,0 hp, betygsskala: 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.
Other requirements for final grade
Written assignments and a project (INL1; 6 credits).
Opportunity to complete the requirements via supplementary examination
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Opportunity to raise an approved grade via renewed examination
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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 web
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.
Course web DD2447Offered by
Main field of study
Computer Science and Engineering
Education cycle
Second cycle
Add-on studies
Please discuss with the course leader.
Contact
Jens Lagergren, e-post: jensl@kth.se
Supplementary information
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex