The programme overview is adopted 2025-02-24 by Dean of Education.

• AUTUMN TERM

  • Study period 1

    • Compulsory courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2025 - aug 2026)
      DAT695 Introduction to data science	Written and oral assignments 4 credits	C
      DAT695 Introduction to data science	Examination 3.5 credits	C	S	Tue 28/10-2025 am J INSP	Tue 13/01-2026 pm J INSP Fri 28/08-2026 pm J INSP
      TMA947 Nonlinear optimisation	Laboratory 1.5 credits	A+
      TMA947 Nonlinear optimisation	Examination 6 credits	A+	S

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2025 - aug 2026)
      DAT246 Empirical software engineering	Written and oral assignments 2.5 credits	A
      DAT246 Empirical software engineering	Examination 5 credits	A	S	Mon 27/10-2025 pm J	Thu 08/01-2026 am J Tue 25/08-2026 am J
      DAT441 Advanced topics in machine learning	Written and oral assignments 3.5 credits	B	1)
      DAT441 Advanced topics in machine learning	Examination 4 credits	B	S, 1)	Mon 27/10-2025 am J INSP	Thu 08/01-2026 pm J INSP Mon 17/08-2026 pm J INSP
      DAT465 Causality and machine learning	Written and oral assignments 7.5 credits		E, 1)
      DAT625 Structured machine learning	Written and oral assignments 7.5 credits	D	E, 1)
      EEN100 Statistics and machine learning in high dimensions	Oral examination 6 credits	A	1)
      EEN100 Statistics and machine learning in high dimensions	Project 1.5 credits	A	S, 1)
      FFR105 Stochastic optimization algorithms	Examination 7.5 credits	D	E	Wed 29/10-2025 am J	Mon 05/01-2026 am J Thu 27/08-2026 am J
      FFR135 Artificial neural networks	Examination 7.5 credits	B	E	Mon 27/10-2025 am J	Wed 07/01-2026 pm J Thu 20/08-2026 pm J
      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	C	E, 1), 2)	Thu 30/10-2025 pm J	Wed 07/01-2026 pm J Fri 21/08-2026 pm J
      SSY340 Deep machine learning	Project 3 credits	A	1)
      SSY340 Deep machine learning	Written and oral assignments 4.5 credits	A	S, 1)
      TIN093 Algorithms	Examination 7.5 credits	A	E, 3)	Sat 25/10-2025 pm J	Thu 27/08-2026 pm J
      TMA265 Numerical linear algebra	Examination 7.5 credits		E
      TMA882 High performance computing	Examination 7.5 credits	B+	E, 1)

  • Study period 2

    • Compulsory courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2025 - aug 2026)
      MVE550 Stochastic processes and Bayesian inference	Examination 6 credits	B		Mon 12/01-2026 am J	Fri 10/04-2026 am J Mon 24/08-2026 am J
      MVE550 Stochastic processes and Bayesian inference	Written and oral assignments 1.5 credits	B	S

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2025 - aug 2026)
      DAT450 Machine learning for natural language processing	Written and oral assignments 7.5 credits	A	S, 1), 4)
      DAT570 Continuous optimization in data science	Written and oral assignments 7.5 credits	A	E, 3)
      EEN020 Computer vision	Project 3 credits	C
      EEN020 Computer vision	Written and oral assignments 4.5 credits	C	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)	Sat 06/12-2025 am J	Wed 08/04-2026 am J Thu 20/08-2026 am J
      MVE190 Statistical learning with regression models	Examination 7.5 credits		E
      SSY130 Applied signal processing	Examination 7.5 credits	D	S	Wed 14/01-2026 pm J	Wed 08/04-2026 pm J Tue 25/08-2026 pm J
      SSY316 Advanced probabilistic machine learning	Project 7.5 credits	B	E, 1)
      TDA251 Algorithms, advanced course	Project 7.5 credits	C	E
      TDA357 Databases	Examination 4.5 credits	D+		Wed 14/01-2026 pm J	Thu 27/08-2026 pm J
      TDA357 Databases	Laboratory 3 credits	D+	S
      TDA507 Computational methods in bioinformatics	Written and oral assignments 7.5 credits	D	E
      TDA596 Distributed systems	Examination 6 credits	C		Tue 13/01-2026 am J INSP	Thu 09/04-2026 pm J INSP Wed 19/08-2026 pm J INSP
      TDA596 Distributed systems	Laboratory 1.5 credits	C	S
      TEK656 Creating technology-based ventures	Examination 7.5 credits		E	Thu 15/01-2026 am J INSP	Wed 08/04-2026 am J INSP Mon 24/08-2026 pm J INSP
      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 2025 - aug 2026)
      DAT410 Design of AI systems	Written and oral assignments 7.5 credits	D	E

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2025 - aug 2026)
      DAT341 Applied machine learning	Take-home examination 4 credits	B	3), 5)
      DAT341 Applied machine learning	Written and oral assignments 3.5 credits	B	S, 3), 5)
      DAT675 Artificial intelligence for molecules	Written and oral assignments 3 credits	A
      DAT675 Artificial intelligence for molecules	Project 4.5 credits	A	S
      EEN210 Applied digital health	Project 5 credits	C+
      EEN210 Applied digital health	Oral examination 2.5 credits	C+	S
      TDA233 Algorithms for machine learning and inference	Written and oral assignments 3 credits	B	3), 5)
      TDA233 Algorithms for machine learning and inference	Examination 4.5 credits	B	S, 3), 5)
      TDA357 Databases	Examination 4.5 credits
      TDA357 Databases	Laboratory 3 credits		S
      TIF150 Information theory for complex systems	Examination 7.5 credits	B+	E
      TIN093 Algorithms	Examination 7.5 credits	A+	E, 3)

  • Study period 4

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2025 - aug 2026)
      DAT341 Applied machine learning	Take-home examination 4 credits	C	3), 5)
      DAT341 Applied machine learning	Written and oral assignments 3.5 credits	C	S, 3), 5)
      DAT471 Computational techniques for large-scale data	Written and oral assignments 4.5 credits	A	3)
      DAT471 Computational techniques for large-scale data	Examination 3 credits	A	S, 3)
      DAT475 Advanced databases	Written and oral assignments 3 credits	D	3)
      DAT475 Advanced databases	Examination 4.5 credits	D	S, 3)		Sat 11/10-2025 am J
      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)
      SSY098 Image analysis	Project 3.5 credits	C	1), 2)
      SSY098 Image analysis	Laboratory 4 credits	C	S, 1), 2)
      TMS016 Spatial statistics and image analysis	Examination 7.5 credits		S, 1)
      TMS088 Financial time series	Examination 7.5 credits		E, 1)

• 1 Compulsory elective: Compulsory elective course. (DAT441, DAT450, DAT465, DAT625, EEN100, MVE172, RRY025, SSY098, SSY316, SSY340, TMA522, TMA882, TMS016, TMS088) 2 of stated courses are required for the degree
• 2 Overlap: Only one of the marked courses can be included in the degree (RRY025, SSY098)
• 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.