Course development and history
A second course in machine learning, giving a broadened and deepened introduction to the area.
Select the semester and course offering above to get information from the correct course syllabus and course offering.
Fundamentals of the probabilistic approach
After successfully taking this course, a student should be able to:
DD2431 Machine learning or the equivalent. SF1901 Probability Theory and
statistics or the equivalent.
No information inserted
"Pattern recognition and Machine Learning", Christopher Bishop
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.
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
Jens Lagergren (firstname.lastname@example.org)
Grading criteria are made available when the course starts.
In this course, the EECS code of honor applies, see:http://www.kth.se/en/eecs/utbildning/hederskodex