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Study at KTH

Courses for Machine Learning

The two-year master's programme in Machine Learning consists of three terms of courses and one final term dedicated to the master's degree project. Each term consist of approximately 30 ECTS credits. The courses presented on this page apply to studies starting in autumn 2024.

Year 1

Courses that run in periods 1 and 2 of Year 2 can potentially be taken in period 1 and period 2 of Year 1 if its leads to a manageable workload for the student.

Apart from the mandatory and conditionally elective course requirements the student is free to choose from all the second cycle and language courses given at KTH to take his/her number of completed course credits to 90 ECTS. First cycle courses may be taken (though we prefer if students take second-cycle courses) but no more than 30 ECTS points can be counted towards graduation. Recommended courses is for those who would like to extend their competency and knowledge in Computer Science and Software Engineering. A final degree project must also be completed.

Students must complete the mandatory courses (A.1.1) and conditionally elective courses. The conditionally elected courses are gouped into two sets; Application Domain (A.1.3), and Theory (A.1.4). A student must complete:

- at least 6 courses from Application Domain and Theory,

with the constraints that

- at least 2 of the 6 courses are from the Theory courses and
- at least 2 of the 6 courses are from the Application Domain courses.

Explicitly this means that students to graduate must have either completed:

- 2 courses from Application Domain and 4 courses from Theory,
- 3 courses from Application Domain and3 courses from Theory,
- 4 courses from Application Domain and 2 courses from Theory.

Apart from the mandatory and conditionally elective courses requirements the student is free to choose from all the second cycle and language courses given at KTH to take the number of completed course credits of 90 ECTS. First cycle courses may be taken (though we prefer if students take scond-cycle courses) but no more that 30 ECTS points can be counted towards graduation. Courses that are not allowed as elective are hobby courses like cooking, bar-tending etc. In section A.1.5 we list a set of recommended courses that students could take especially those who would like to extend theid competency and knowledge in Computer Science and Software Engineering. A final degree project (A.1.2) must also be completed.

Students who in a previous degree have read a course correspinding to DD1420, DD2380 or DD2434 may apply to read a replacement course instead. The application is submitted to the master coordinator who, after reviewing the previously read course, gives persmission for the student to take a replacement cours from the set of conditionally elective or recommended courses. The course replacement course, if it is a conditionally elective course, will not count towards one of the 6 conditionally elective course requirements. 

Student who completed their first three years of study at KTH within the programme CINTE, who have read ID1214 Artificial Intelligence and Applications, can apply to read a replacement course. Contact the master coordinator according to the instruction above.

Mandatory courses

Conditionally elective courses

Year 2

Conditionally elective courses

Machine Learning

Admissions

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