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DD2434 Machine Learning, Advanced Course 7.5 credits

A second course in machine learning, giving a broadened and deepened introduction to the area.

Information per course offering

Termin

Information for Autumn 2024 mladv24 programme students

Course location

KTH Campus

Duration
28 Oct 2024 - 13 Jan 2025
Periods
P2 (7.5 hp)
Pace of study

50%

Application code

52222

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group

Open for all students from year 3 and for students admitted to a master's programme, as long as it can be included in your programme.

Planned modular schedule
[object Object]
Part of programme

Master's Programme, Applied and Computational Mathematics, åk 1, Optional

Master's Programme, Applied and Computational Mathematics, åk 2, Optional

Master's Programme, Computer Science, åk 2, CSCS, Recommended

Master's Programme, Computer Science, åk 2, CSDA, Conditionally Elective

Master's Programme, Computer Simulations for Science and Engineering, åk 2, Conditionally Elective

Master's Programme, Cybersecurity, åk 1, Recommended

Master's Programme, Cybersecurity, åk 2, Recommended

Master's Programme, ICT Innovation, åk 1, DASC, Recommended

Master's Programme, ICT Innovation, åk 1, DASE, Recommended

Master's Programme, ICT Innovation, åk 2, DASC, Recommended

Master's Programme, ICT Innovation, åk 2, DASE, Recommended

Master's Programme, Industrial Engineering and Management, åk 1, MAIG, Conditionally Elective

Master's Programme, Information and Network Engineering, åk 2, Recommended

Master's Programme, Information and Network Engineering, åk 2, INF, Recommended

Master's Programme, Machine Learning, åk 1, Mandatory

Master's Programme, Mechatronics, åk 1, Conditionally Elective

Master's Programme, Mechatronics, åk 2, Conditionally Elective

Master's Programme, Systems, Control and Robotics, åk 1, LDCS, Conditionally Elective

Master's Programme, Systems, Control and Robotics, åk 1, RASM, Conditionally Elective

Master's Programme, Systems, Control and Robotics, åk 2, LDCS, Conditionally Elective

Master's Programme, Systems, Control and Robotics, åk 2, RASM, Conditionally Elective

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted
Contact

Jens Lagergren (jensl@kth.se)

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus DD2434 (Autumn 2024–)
Headings with content from the Course syllabus DD2434 (Autumn 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

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

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 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.

Literature and preparations

Specific prerequisites

  • Knowledge and skills in Programming, 6 credits, corresponding to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD100N/ID1018.
  • Knowledge in Linear Algebra, 7,5 credits, corresponding to completed course SF1624/SF1672/SF1684.
  • Knowledge in Calculus in Several Variables, 7,5 credits, corresponding to completed course SF1626/SF1674.
  • Knowledge in Probability Theory and Statistics, 6 credits, corresponding to completed course SF1910-SF1924/SF1935

and at least one of the following:

either

  • Knowledge of basic Machine Learning, 7,5 credits, corresponding to completed course DD1420/DD2421/EL2810/EQ2341

or

  • Knowledge of higher mathematics relevant to advanced Machine Learning, 15 credits, e.g. corresponding to completed courses SF2940 Probability Theory and SF2955 Computer Intensive Methods in Mathematical Statistics.
  • Knowledge of basic computer science, 6 credits, corresponding to completed course DD1338/DD1320-DD1328/DD2325/ID1020/ID1021.
  • Additional skills in independent software development, 12 credits, from completed courses in computer science, computer technology or numerical methods with laboratory elements that are not carried out in groups larger than two people. These courses are in addition to the above mentioned courses.

Active participation in a course offering where the final examination is not yet reported in Ladok is considered equivalent to completion of the course.
Registering for a course is counted as active participation.
The term 'final examination' encompasses both the regular examination and the first re-examination.

Recommended prerequisites

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.

Equipment

No information inserted

Literature

No information inserted

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

  • HEM1 - Take-home exam, 3.5 credits, grading scale: A, B, C, D, E, FX, F
  • PRO1 - Project assignment, 4.0 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.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

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

Add-on studies

No information inserted

Contact

Jens Lagergren (jensl@kth.se)

Transitional regulations

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

Supplementary information

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