General Courses
Study year 1
Mandatory courses (31.5 hp)
| Course code | Course name | Credits | Edu. level |
|---|---|---|---|
| DA2205 | Introduction to the Philosophy of Science and Research Methodology | 7.5 hp | Second cycle |
| DD1420 | Foundations of Machine LearningOverlaps with DD2421 | 7.5 hp | First cycle |
| DD2301 | Program Integrating Course in Machine Learning | 3.0 hp | Second cycle |
| DD2380 | Artificial IntelligenceOverlaps with ID1214 at CINTE | 6.0 hp | Second cycle |
| DD2434 | Machine Learning, Advanced Course | 7.5 hp | Second cycle |
Conditionally elective courses
| Course code | Course name | Credits | Edu. level |
|---|---|---|---|
| DD2257 | VisualizationIncluded in Application Domain Visualization | 7.5 hp | Second cycle |
| DD2401 | NeuroscienceIncluded in Application Domain, Computational Biology | 7.5 hp | Second cycle |
| DD2402 | Advanced Individual Course in Computational BiologyIncluded in Application Domain, Computational Biology | 6.0 hp | Second cycle |
| DD2410 | Introduction to RoboticsIncluded in Application Domain, Robotics | 7.5 hp | Second cycle |
| DD2411 | Research project in Robotics, Perception and LearningIncluded in Application Domain, Robotics | 15.0 hp | Second cycle |
| DD2417 | Language EngineeringIncluded in Application Domain Language Processing; Speech & Text | 7.5 hp | Second cycle |
| DD2419 | Project Course in Robotics and Autonomous SystemsIncluded in Application Domain, Robotics | 9.0 hp | Second cycle |
| DD2420 | Probabilistic Graphical ModelsIncluded in Theory, Statistics & Probability (may not be combined with DD2447) | 7.5 hp | Second cycle |
| DD2423 | Image Analysis and Computer VisionIncluded in Application Domain Computer Vision | 7.5 hp | Second cycle |
| DD2424 | Deep Learning in Data ScienceIncluded in Application Domain Computer Vision | 7.5 hp | Second cycle |
| DD2435 | Mathematical Modelling of Biological SystemsIncluded in Application Domain, Computational Biology | 9.0 hp | Second cycle |
| DD2437 | Artificial Neural Networks and Deep ArchitecturesIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| DD2438 | Artificial Intelligence and Multi Agent SystemsIncluded in Application Domain, Robotic | 15.0 hp | Second cycle |
| DD2447 | Statistical Methods in Applied Computer ScienceIncluded in Theory, Statistics & Probability (may not be combined with DD2420) | 6.0 hp | Second cycle |
| DD2477 | Search Engines and Information Retrieval SystemsIncluded in Application Domain Databases/Information Retrieval | 7.5 hp | Second cycle |
| DT2112 | Speech TechnologyIncluded in Application Domain, Language Processing; Speech & Text | 7.5 hp | Second cycle |
| DT2119 | Speech and Speaker RecognitionIncluded in Application Domain, Language Processing; Speech & Text | 7.5 hp | Second cycle |
| DT2470 | Music InformaticsIncluded in Application Domain, Sound | 7.5 hp | Second cycle |
| EL2320 | Applied EstimationIncluded in Theory, Mathematics | 7.5 hp | Second cycle |
| EL2805 | Reinforcement LearningIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| EL2810 | Machine Learning TheoryIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| EQ2341 | Pattern Recognition and Machine LearningIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| EQ2425 | Analysis and Search of Visual DataIncluded in Application Domain Computer Vision | 7.5 hp | Second cycle |
| ID2222 | Data MiningIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| ID2223 | Scalable Machine Learning and Deep LearningIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| SF1811 | OptimizationIncluded in Theory, Mathematics | 6.0 hp | First cycle |
| SF2930 | Regression AnalysisIncluded in Theory, Statistics & Probability | 7.5 hp | Second cycle |
| SF2940 | Probability TheoryIncluded in Theory, Statistics & Probability | 7.5 hp | Second cycle |
| SF2943 | Time Series AnalysisIncluded in Theory, Statistics & Probability | 7.5 hp | Second cycle |
Recommended courses
| Course code | Course name | Credits | Edu. level |
|---|---|---|---|
| DD1388 | Program System Construction Using C++ | 7.5 hp | First cycle |
| DD2352 | Algorithms and Complexity | 7.5 hp | Second cycle |
| DD2395 | Computer Security | 6.0 hp | Second cycle |
| DD2448 | Foundations of Cryptography | 7.5 hp | Second cycle |
| DH2642 | Interaction Programming and the Dynamic Web | 7.5 hp | Second cycle |
| ID2221 | Data-Intensive Computing | 7.5 hp | Second cycle |
| IK2215 | Advanced Internetworking | 7.5 hp | Second cycle |
| IK2221 | Networked Systems for Machine Learning | 7.5 hp | Second cycle |
| IK2227 | Network Systems with Edge or Cloud Datacenters | 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 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
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.
Study year 2
Mandatory courses (33.0 hp)
| Course code | Course name | Credits | Edu. level |
|---|---|---|---|
| DA233X | Degree Project in Computer Science and Engineering, specializing in Machine Learning, Second Cycle | 30.0 hp | Second cycle |
| DD2301 | Program Integrating Course in Machine Learning | 3.0 hp | Second cycle |
Conditionally elective courses
| Course code | Course name | Credits | Edu. level |
|---|---|---|---|
| DD2257 | VisualizationIncluded in Application Domain Visualization | 7.5 hp | Second cycle |
| DD2410 | Introduction to RoboticsIncluded in Application Domain, Robotics | 7.5 hp | Second cycle |
| DD2411 | Research project in Robotics, Perception and LearningIncluded in Application Domain, Robotics | 15.0 hp | Second cycle |
| DD2420 | Probabilistic Graphical ModelsIncluded in Theory, Statistics & Probability (may not be combined with DD2447) | 7.5 hp | Second cycle |
| DD2423 | Image Analysis and Computer VisionIncluded in Application Domain Computer Vision | 7.5 hp | Second cycle |
| DD2430 | Project Course in Data ScienceIncluded in Application Domain | 7.5 hp | Second cycle |
| DD2435 | Mathematical Modelling of Biological SystemsIncluded in Application Domain, Computational Biology | 9.0 hp | Second cycle |
| DD2437 | Artificial Neural Networks and Deep ArchitecturesIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| DD2438 | Artificial Intelligence and Multi Agent SystemsIncluded in Application Domain, Robotics | 15.0 hp | Second cycle |
| DD2447 | Statistical Methods in Applied Computer ScienceIncluded in Theory, Statistics & Probability (may not be combined with DD2420) | 6.0 hp | Second cycle |
| DD2601 | Deep Generative Models and SynthesisIncluded in Theory | 7.5 hp | Second cycle |
| DD2610 | Deep Learning, advanced courseIncluded in Theory | 7.5 hp | Second cycle |
| DT2470 | Music InformaticsIncluded in Application Domain, Sound | 7.5 hp | Second cycle |
| EL2320 | Applied EstimationIncluded in Theory, Mathematics | 7.5 hp | Second cycle |
| EL2805 | Reinforcement LearningIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| EQ2425 | Analysis and Search of Visual DataIncluded in Application Domain Computer Vision | 7.5 hp | Second cycle |
| ID2222 | Data MiningIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| ID2223 | Scalable Machine Learning and Deep LearningIncluded in Theory, Machine Learning | 7.5 hp | Second cycle |
| SF1811 | OptimizationIncluded in Theory, Mathematics | 6.0 hp | First cycle |
| SF2930 | Regression AnalysisIncluded in Theory, Statistics & Probability | 7.5 hp | Second cycle |
| SF2940 | Probability TheoryIncluded in Theory, Statistics & Probability | 7.5 hp | Second cycle |
Recommended courses
| Course code | Course name | Credits | Edu. level |
|---|---|---|---|
| DD1388 | Program System Construction Using C++ | 7.5 hp | First cycle |
| DD2352 | Algorithms and Complexity | 7.5 hp | Second cycle |
| DD2395 | Computer Security | 6.0 hp | Second cycle |
| DD2448 | Foundations of Cryptography | 7.5 hp | Second cycle |
| DH2642 | Interaction Programming and the Dynamic Web | 7.5 hp | Second cycle |
| ID2221 | Data-Intensive Computing | 7.5 hp | Second cycle |
| IK2215 | Advanced Internetworking | 7.5 hp | Second cycle |
| IK2221 | Networked Systems for Machine Learning | 7.5 hp | Second cycle |
| IK2227 | Network Systems with Edge or Cloud Datacenters | 7.5 hp | Second cycle |
| SF2568 | Parallel Computations for Large- Scale Problems | 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.