The following courses are part of study year two.
The course application codes and study periods are valid for the academic year 2026/2027. For other academic years, different application codes and study periods may apply
General Courses
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.
Mandatory courses
| Course code and name | Appl.code | Scope | P1 | P2 | P3 | P4 |
|---|---|---|---|---|---|---|
| DD2301 Program Integrating Course in Machine Learning | 50377 | 3.0 credits | 0.5 | 0.5 | ||
| DA233X Degree Project in Computer Science and Engineering, specializing in Machine Learning, Second Cycle | 30.0 credits |
Choose among the conditionally elective courses, so that the following conditions are fulfilled:
- at least 6 courses from Application Domains + Theory, and
- at least 2 courses from Application Domains, and also
- at least 2 courses from Theory.
Examples of possible combinations of courses:
- at least 2 courses from Application Domains, and at least 4 courses from Theory,
- at least 3 courses from Application Domains, and at least 3 courses from Theory,
- at least 4 courses from Application Domains, and at least 2 courses from Theory.
Conditionally elective courses
| Course code and name | Appl.code | Scope | P1 | P2 | P3 | P4 |
|---|---|---|---|---|---|---|
| DD2411 Research project in Robotics, Perception and LearningIncluded in Application Domain, Robotics | 60325 | 15.0 credits | 4.0 | 3.5 | ||
| DD2257 VisualizationIncluded in Application Domain Visualization | 7.5 credits | |||||
| DD2410 Introduction to RoboticsIncluded in Application Domain, Robotics | 7.5 credits | |||||
| DD2420 Probabilistic Graphical ModelsIncluded in Theory, Statistics & Probability (may not be combined with DD2447) | 7.5 credits | |||||
| DD2423 Image Analysis and Computer VisionIncluded in Application Domain Computer Vision | 7.5 credits | |||||
| DD2430 Project Course in Data ScienceIncluded in Application Domain | 7.5 credits | |||||
| DD2435 Mathematical Modelling of Biological SystemsIncluded in Application Domain, Computational Biology | 9.0 credits | |||||
| DD2437 Artificial Neural Networks and Deep ArchitecturesIncluded in Theory, Machine Learning | 7.5 credits | |||||
| DD2438 Artificial Intelligence and Multi Agent SystemsIncluded in Application Domain, Robotics | 15.0 credits | |||||
| DD2447 Statistical Methods in Applied Computer ScienceIncluded in Theory, Statistics & Probability (may not be combined with DD2420) | 6.0 credits | |||||
| DD2601 Deep Generative Models and SynthesisIncluded in Theory | 7.5 credits | |||||
| DD2610 Deep Learning, advanced courseIncluded in Theory | 7.5 credits | |||||
| DT2470 Music InformaticsIncluded in Application Domain, Sound | 7.5 credits | |||||
| EL2320 Applied EstimationIncluded in Theory, Mathematics | 7.5 credits | |||||
| EL2805 Reinforcement LearningIncluded in Theory, Machine Learning | 7.5 credits | |||||
| EQ2425 Analysis and Search of Visual DataIncluded in Application Domain Computer Vision | 7.5 credits | |||||
| ID2222 Data MiningIncluded in Theory, Machine Learning | 7.5 credits | |||||
| ID2223 Scalable Machine Learning and Deep LearningIncluded in Theory, Machine Learning | 7.5 credits | |||||
| SF1811 OptimizationIncluded in Theory, Mathematics | 6.0 credits | |||||
| SF2930 Regression AnalysisIncluded in Theory, Statistics & Probability | 7.5 credits | |||||
| SF2940 Probability TheoryIncluded in Theory, Statistics & Probability | 7.5 credits |
Recommended courses
| Course code and name | Appl.code | Scope | P1 | P2 | P3 | P4 |
|---|---|---|---|---|---|---|
| DD1388 Program System Construction Using C++ | 7.5 credits | |||||
| DD2352 Algorithms and Complexity | 7.5 credits | |||||
| DD2395 Computer Security | 6.0 credits | |||||
| DD2448 Foundations of Cryptography | 7.5 credits | |||||
| DH2642 Interaction Programming and the Dynamic Web | 7.5 credits | |||||
| ID2221 Data-Intensive Computing | 7.5 credits | |||||
| IK2215 Advanced Internetworking | 7.5 credits | |||||
| IK2221 Networked Systems for Machine Learning | 7.5 credits | |||||
| IK2227 Network Systems with Edge or Cloud Datacenters | 7.5 credits | |||||
| SF2568 Parallel Computations for Large- Scale Problems | 7.5 credits |