DD2434 Machine Learning, Advanced Course 7.5 credits

• The basics of the probabilistic method.
• Probabilistic modelling.
• Dimensionality reduction.
• Graphical models.
• Hidden Markov models.
• Expectation-Maximization.
• Variational Inference.
• Networks in variational inference.

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 deterministic 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 do a degree project in deterministic inference methods.

Knowledge and skills in programming, 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD1333/DD100N/ID1018/ID1022.

Knowledge in linear algebra, 7.5 credits, equivalent to completed course SF1624/SF1672/SF1684.

Knowledge in calculus in several variables, 7.5 credits, equivalent to completed course SF1626/SF1674/SF1686.

Knowledge in probability theory and statistics, 6 credits, equivalent to completed course SF1910-SF1925/SF1935 or completed course component TEN1 within SF1910/SF1925/SF1935.

In addition, at least one of the following:
either

• knowledge in basic machine learning, 7.5 credits, equivalent to completed course DD1420/DD2421/EL2810/EQ2341

or both of the following:

• knowledge in higher mathematics relevant to advanced machine learning, 15 credits, e.g. equivalent to completed courses SF2940 Probability Theory and SF2955 Computer Intensive Methods in Mathematical Statistics
• knowledge in algorithms, data structures and basic software development techniques, 6 credits, equivalent to completed course DD1338/DD1320-DD1328/DD2325/ID1020/ID1021 or completed course components KONT and LABD in DD1326.

Active participation in DD1420/DD2421/SF2940 during study period 1 of the same academic year is equivalent to completed course. Anyone who is registered is expected and considered to be actively participating.

For KTH students, the recommended preparation is DD1420.

Also DD2421 and EL2810 are accepted as special eligibility requirements, but more time and effort may be required to complete the course.

• TENH - Written Exam, 0.0 credits, grading scale: A, B, C, D, E, FX, F
• LABA - Laboratory Tasks, 7.5 credits, grading scale: P, 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.

Passing the course component LABA gives a grade of E on the course. TENH is an optional exam that is only required for grades higher than E.

Jens Lagergren

• 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.

TEN1 is replaced by HEM1 and LAB1 is replaced by PRO1.

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