General courses

Year 1

Conditionally elective courses

Code Name Credits Edu. level
DD2257 Visualization Included in Application Domain Visualization 7.5 hp Second cycle
DD2401 Neuroscience Included in Application Domain, Computational Biology 7.5 hp Second cycle
DD2402 Advanced Individual Course in Computational Biology Included in Application Domain, Computational Biology 6.0 hp Second cycle
DD2410 Introduction to Robotics Included in Application Domain, Robotics 7.5 hp Second cycle
DD2411 Research project in Robotics, Perception and Learning Included in Application Domain, Robotics 15.0 hp Second cycle
DD2412 Deep Learning, Advanced Course Included in Theory, Machine Learning 6.0 hp Second cycle
DD2418 Language Engineering Included in Application Domain Language Processing; Speech & Text 6.0 hp Second cycle
DD2419 Project Course in Robotics and Autonomous Systems Included in Application Domain, Robotics 9.0 hp Second cycle
DD2420 Probabilistic Graphical Models Included in Theory, Statistics & Probability 7.5 hp Second cycle
DD2423 Image Analysis and Computer Vision Included in Application Domain Computer Vision 7.5 hp Second cycle
DD2424 Deep Learning in Data Science Included in Application Domain Computer Vision 7.5 hp Second cycle
DD2435 Mathematical Modelling of Biological Systems Included in Application Domain, Computational Biology 9.0 hp Second cycle
DD2437 Artificial Neural Networks and Deep Architectures Included in Theory, Machine Learning 7.5 hp Second cycle
DD2438 Artificial Intelligence and Multi Agent Systems Included in Application Domain, Robotic 15.0 hp Second cycle
DD2447 Statistical Methods in Applied Computer Science Included in Theory, Statistics & Probability 6.0 hp Second cycle
DD2476 Search Engines and Information Retrieval Systems Included in Application Domain Databases/Information Retrieval 9.0 hp Second cycle
DT2112 Speech Technology Included in Application Domain, Language Processing; Speech & Text 7.5 hp Second cycle
DT2119 Speech and Speaker Recognition Included in Application Domain, Language Processing; Speech & Text 7.5 hp Second cycle
EL2320 Applied Estimation Included in Theory, Mathematics 7.5 hp Second cycle
EL2805 Reinforcement Learning Included in Theory, Machine Learning 7.5 hp Second cycle
EQ2341 Pattern Recognition and Machine Learning Included in Theory, Machine Learning 7.5 hp Second cycle
EQ2425 Analysis and Search of Visual Data Included in Application Domain Computer Vision 7.5 hp Second cycle
ID2222 Data Mining Included in Theory, Machine Learning 7.5 hp Second cycle
ID2223 Scalable Machine Learning and Deep Learning Included in Theory, Machine Learning 7.5 hp Second cycle
SF1811 Optimization Included in Theory, Mathematics 6.0 hp First cycle
SF2930 Regression Analysis Included in Theory, Statistics & Probability 7.5 hp Second cycle
SF2940 Probability Theory Included in Theory, Statistics & Probability 7.5 hp Second cycle
SF2943 Time Series Analysis Included in Theory, Statistics & Probability 7.5 hp Second cycle

Supplementary information

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.

Information regarding conditionally elective courses

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.

Year 2

Conditionally elective courses

Code Name Credits Edu. level
DD2257 Visualization Included in Application Domain Visualization 7.5 hp Second cycle
DD2410 Introduction to Robotics Included in Application Domain, Robotics 7.5 hp Second cycle
DD2411 Research project in Robotics, Perception and Learning Included in Application Domain, Robotics 15.0 hp Second cycle
DD2412 Deep Learning, Advanced Course Included in Theory, Machine Learning 6.0 hp Second cycle
DD2419 Project Course in Robotics and Autonomous Systems Included in Application Domain, Robotics 9.0 hp Second cycle
DD2420 Probabilistic Graphical Models Included in Theory, Machine Learning 7.5 hp Second cycle
DD2423 Image Analysis and Computer Vision Included in Application Domain Computer Vision 7.5 hp Second cycle
DD2430 Project Course in Data Science Included in Application Domain 7.5 hp Second cycle
DD2435 Mathematical Modelling of Biological Systems Included in Application Domain, Computational Biology 9.0 hp Second cycle
DD2437 Artificial Neural Networks and Deep Architectures Included in Theory, Machine Learning 7.5 hp Second cycle
DD2438 Artificial Intelligence and Multi Agent Systems Included in Application Domain, Robotics 15.0 hp Second cycle
DD2447 Statistical Methods in Applied Computer Science Included in Theory, Statistics & Probability 6.0 hp Second cycle
EL2320 Applied Estimation Included in Theory, Mathematics 7.5 hp Second cycle
EL2805 Reinforcement Learning Included in Theory, Machine Learning 7.5 hp Second cycle
EQ2425 Analysis and Search of Visual Data Included in Application Domain Computer Vision 7.5 hp Second cycle
ID2222 Data Mining Included in Theory, Machine Learning 7.5 hp Second cycle
ID2223 Scalable Machine Learning and Deep Learning Included in Theory, Machine Learning 7.5 hp Second cycle
SF1811 Optimization Included in Theory, Mathematics 6.0 hp First cycle
SF2930 Regression Analysis Included in Theory, Statistics & Probability 7.5 hp Second cycle
SF2940 Probability Theory Included in Theory, Statistics & Probability 7.5 hp Second cycle

Supplementary information

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.

Information regarding conditionally elective courses

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.