The programme overview is adopted 2026-02-17 by Dean of Education.

• AUTUMN TERM

  • Study period 1

    • Compulsory courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      DAT695 Introduction to data science	Written and oral assignments 4 credits
      DAT695 Introduction to data science	Examination 3.5 credits		S
      TMA947 Nonlinear optimisation	Laboratory 1.5 credits
      TMA947 Nonlinear optimisation	Examination 6 credits		S

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      DAT060 Logic in computer science	Examination 7.5 credits		E
      DAT246 Empirical software engineering	Written and oral assignments 2.5 credits
      DAT246 Empirical software engineering	Examination 5 credits		S
      DAT300 Data-driven support for cyber-physical systems	Project 7.5 credits		E
      DAT400 High-performance parallel programming	Laboratory 3 credits
      DAT400 High-performance parallel programming	Examination 4.5 credits		S
      DAT441 Advanced topics in machine learning	Written and oral assignments 3.5 credits		1)
      DAT441 Advanced topics in machine learning	Examination 4 credits		S, 1)
      DAT625 Structured machine learning	Written and oral assignments 7.5 credits		E, 1)
      EEN100 Statistics and machine learning in high dimensions	Oral examination 6 credits		1)
      EEN100 Statistics and machine learning in high dimensions	Project 1.5 credits		S, 1)
      FFR105 Stochastic optimization algorithms	Examination 7.5 credits		E
      FFR135 Artificial neural networks	Examination 7.5 credits		E
      MVE188 Computational methods for Bayesian statistics	Project 2 credits
      MVE188 Computational methods for Bayesian statistics	Examination 5.5 credits		S
      RRY025 Image processing	Examination 7.5 credits		E, 1), 2)
      SSY340 Deep machine learning	Project 3 credits		1)
      SSY340 Deep machine learning	Written and oral assignments 4.5 credits		S, 1)
      TIN093 Algorithms	Examination 7.5 credits		E, 3)
      TMA265 Numerical linear algebra	Examination 7.5 credits		E
      TMA883 High performance computing	Written and oral assignments 7.5 credits		E, 1)
      TMS150 Stochastic data processing and simulation	Project 7.5 credits		E

  • Study period 2

    • Compulsory courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      MVE550 Stochastic processes and Bayesian inference	Examination 6 credits
      MVE550 Stochastic processes and Bayesian inference	Written and oral assignments 1.5 credits		S

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      DAT295 Autonomous and cooperative vehicular systems	Project 7.5 credits		E
      DAT315 The computer scientist in society	Written and oral assignments 7.5 credits		E
      DAT415 Computability	Examination 4.5 credits
      DAT415 Computability	Written and oral assignments 3 credits		S
      DAT450 Machine learning for natural language processing	Written and oral assignments 7.5 credits		S, 1), 4)
      DAT570 Continuous optimization in data science	Written and oral assignments 7.5 credits		E, 3)
      DAT615 Neuro-symbolic AI	Examination 7.5 credits		E
      EEN020 Computer vision	Project 3 credits
      EEN020 Computer vision	Written and oral assignments 4.5 credits		S
      MVE095 Options and mathematics	Examination 7.5 credits		E, 3)
      MVE172 Basic stochastic processes and financial applications	Laboratory 3 credits		1)
      MVE172 Basic stochastic processes and financial applications	Examination 4.5 credits		S, 1)
      MVE190 Statistical learning with regression models	Examination 7.5 credits		E
      SSY130 Applied signal processing	Examination 7.5 credits		S
      SSY317 Advanced probabilistic machine learning	Examination 7.5 credits		E, 1)
      TDA251 Algorithms, advanced course	Project 7.5 credits		E
      TDA352 Cryptography	Laboratory 1.5 credits
      TDA352 Cryptography	Examination 6 credits		S
      TDA357 Databases	Examination 4.5 credits
      TDA357 Databases	Laboratory 3 credits		S
      TDA507 Computational methods in bioinformatics	Written and oral assignments 7.5 credits		E
      TDA596 Distributed systems	Examination 6 credits
      TDA596 Distributed systems	Laboratory 1.5 credits		S
      TEK010 Creating new business	Examination 7.5 credits		E
      TMA522 Large scale optimization	Examination (part A) 6 credits		1)
      TMA522 Large scale optimization	Project (part B) 1.5 credits		S, 1)

• SPRING TERM

  • Study period 3

    • Compulsory courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      DAT410 Design of AI systems	Written and oral assignments 7.5 credits		E

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      DAT341 Applied machine learning	Take-home examination 4 credits		3), 5)
      DAT341 Applied machine learning	Written and oral assignments 3.5 credits		S, 3), 5)
      DAT675 Artificial intelligence for molecules	Written and oral assignments 3 credits
      DAT675 Artificial intelligence for molecules	Project 4.5 credits		S
      EEN210 Applied digital health	Project 5 credits
      EEN210 Applied digital health	Oral examination 2.5 credits		S
      TDA233 Algorithms for machine learning and inference	Written and oral assignments 3 credits		3), 5)
      TDA233 Algorithms for machine learning and inference	Examination 4.5 credits		S, 3), 5)
      TDA357 Databases	Examination 4.5 credits
      TDA357 Databases	Laboratory 3 credits		S
      TIN093 Algorithms	Examination 7.5 credits		E, 3)

  • Study period 4

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      DAT341 Applied machine learning	Take-home examination 4 credits		3), 5)
      DAT341 Applied machine learning	Written and oral assignments 3.5 credits		S, 3), 5)
      DAT471 Computational techniques for large-scale data	Written and oral assignments 4.5 credits		3)
      DAT471 Computational techniques for large-scale data	Examination 3 credits		S, 3)
      DAT475 Advanced databases	Written and oral assignments 3 credits		3)
      DAT475 Advanced databases	Examination 4.5 credits		S, 3)
      DAT530 Research-oriented course in data science and AI	Project 7.5 credits		E
      MVE166 Linear and integer optimization with applications	Examination (part A) 6 credits
      MVE166 Linear and integer optimization with applications	Project (part B) 1.5 credits		S
      MVE441 Statistical learning for big data	Project 1.5 credits		3)
      MVE441 Statistical learning for big data	Take-home examination 6 credits		S, 3)
      SSY099 Image analysis	Project 3.5 credits		1), 2)
      SSY099 Image analysis	Laboratory 4 credits		S, 1), 2)
      TMS016 Spatial statistics and image analysis	Examination 7.5 credits		S, 1)
      TMS089 Time series analysis with financial applications	Examination (part A) 6 credits		1)
      TMS089 Time series analysis with financial applications	Project (part B) 1.5 credits		S, 1)

• 1 Compulsory elective: Compulsory elective course. (DAT441, DAT450, DAT625, EEN100, MVE172, RRY025, SSY099, SSY317, SSY340, TMA522, TMA883, TMS016, TMS089) 2 of stated courses are required for the degree
• 2 Overlap: Only one of the marked courses can be included in the degree (RRY025, SSY099)
• 3 Compulsory elective: Compulsory elective course. (DAT341, DAT471, DAT475, DAT570, MVE095, MVE441, TDA233, TIN093) 2 of stated courses are required for the degree
• 4 Recommendation: The course is normally followed during the second year of the Masters programme (DAT450)
• 5 Overlap: Only one of the marked courses can be included in the degree (DAT341, TDA233)
• DIG: Digital examination is an examination written in the Inspera system. The student will bring their own computer and access the exam via Safe exam browser.
• S: Final grade. All module grades are reported before the final grade for the course can be reported.
• E: The only module in the course. Module grade and grade for the course are reported at the same time.