Course development and history
This course summarizes statistical and probabilistic methods used in applied Computer Science.
Select the semester and course offering above to get information from the correct course syllabus and course offering.
Basic statistical concepts and basic probability theory.
Directed graphical models.
Undirected graphical models.
Exact inference for graphical models.
State space models.
Monte Carlo estimation.
Sequential Monte Carlo.
Markov Chain Mote Carlo.
The Dirichlet process.
After successfully taking this course, a student should be able to:
explain and justify several important machine learning methods,
account for several types of methods and algorithms used in the field, implement them using the book, and extend and modify them,
critically evaluate the methods’ applicability in new contexts and construct new applications,
follow research and development in the area.
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Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology.
Courses in mathematics (analysis), programming, computer science and statistics equivalent to obligatory courses on D- or F-programme.Matlab or similar tool (Octave, R).
Machine Learning A Probabilistic Perspective av Kevin P. Murphy.
Kevin P. Murphy's "Machine Learning A Probabilistic Perspective"
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.
Assignments and a project (INL1; 6 university credits)
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.
Computer Science and Engineering
Please discuss with the course leader.
Jens Lagergren, e-post: email@example.com
In this course, the EECS code of honor applies, see:http://www.kth.se/en/eecs/utbildning/hederskodex