Program Overview

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

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Information for Autumn 2026 mladv26 Programme Students

• Course location: KTH Campus
• Duration: 26 Oct 2026 - 11 Jan 2027
• Periods: Autumn 2026: P2 (7.5 hp)
• Pace of study: 50%
• Application code: 11585
• Form of study: Normal Daytime
• Language of instruction: English
• Course memo: Course memo is not published
• Number of places: Min: 1
• 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.

Part of Programme

The course is part of the following programmes:

• Master's Programme, ICT Innovation, year 1, DASE
• Master's Programme, Information and Network Engineering, year 2, INF
• Master's Programme, Systems, Control and Robotics, year 2, RASM
• Master's Programme, Computer Science, year 2, CSDA
• Master of Science in Engineering and in Education, year 6, TEDA
• Master's Programme, Systems, Control and Robotics, year 1, RASM
• Master's Programme, Applied and Computational Mathematics, year 2, CSSE
• Master's Programme, Energy Innovation, year 1, ESAI
• Master of Science in Engineering and in Education, year 5, TEDA
• Master's Programme, ICT Innovation, year 1, DASC
• Master's Programme, Systems, Control and Robotics, year 1, LDCS
• Master's Programme, Systems, Control and Robotics, year 2, LDCS
• Master's Programme, Industrial Engineering and Management, year 1, MAIG
• Master's Programme, ICT Innovation, year 2, DASE
• Master's Programme, Computer Science, year 2, CSCS
• Master's Programme, ICT Innovation, year 2, DASC
• Master's Programme, Applied and Computational Mathematics, year 1
• Master's Programme, Cybersecurity, year 1
• Master's Programme, Applied and Computational Mathematics, year 2
• Master's Programme, Cybersecurity, year 2
• Master's Programme, Biostatistics and Data Science, year 2
• Master's Programme, Information and Network Engineering, year 2
• Master's Programme, Machine Learning, year 1, Mandatory

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

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
• Knowledge of basic Machine Learning, 7.5 credits, corresponding to completed course DD1420/DD2421/EL2810/EQ2341
• 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

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.

Examination and Completion

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

Further Information

Course Room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas.

Offered By

EECS/Intelligent Systems

Main Field of Study

Computer Science and Engineering

Education Cycle

Second cycle

Supplementary Information

Grading criteria are made available when the course starts. In this course, the EECS code of honor applies.