Year 1

Programme overview for year 2026/2027

The programme overview is adopted 2026-02-19 by Dean of Education.

Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
FFR105 Stochastic optimization algorithms	Examination 7.5 credits		E
FFR135 Artificial neural networks	Examination 7.5 credits		E

Course code and name	Module, credits/period	Note
EEN100 Statistics and machine learning in high dimensions	Oral examination 6 credits	1), 2)
EEN100 Statistics and machine learning in high dimensions	Project 1.5 credits	S, 1), 2)
KMG060 Systems biology	Examination 7.5 credits	S, 1), 2)
RRY025 Image processing	Examination 7.5 credits	E, 1), 2)
TIF160 Humanoid robotics	Project 7.5 credits	E, 1), 2)
TIF295 Experimental methods in modern physics	Project 3 credits	E, 1), 2)
TIF430 Quantum mechanics	Oral examination 4.5 credits	E, 1), 2)
TIN093 Algorithms	Examination 7.5 credits	E, 1), 2)
TMA883 High performance computing	Written and oral assignments 7.5 credits	E, 1), 2)
TMA947 Nonlinear optimisation	Laboratory 1.5 credits	1), 2)
TMA947 Nonlinear optimisation	Examination 6 credits	S, 1), 2)
TMS165 Stochastic calculus	Examination 7.5 credits	E, 1), 2)

Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
FFR120 Simulation of complex systems	Project 7.5 credits		E
TIF155 Dynamical systems	Examination 7.5 credits		E

Course code and name	Module, credits/period	Note
DAT450 Machine learning for natural language processing	Written and oral assignments 7.5 credits	S
ENM140 Game theory and rationality	Project 7.5 credits	E, 1), 2)
FKA122 Computational physics	Project 7.5 credits	E, 1), 2)
MTF073 Computational fluid dynamics: The finite volume method (CFD)	Written and oral assignments (part A) 1.5 credits	1), 2)
MTF073 Computational fluid dynamics: The finite volume method (CFD)	Written and oral assignments (part B) 1.5 credits	1), 2)
MTF073 Computational fluid dynamics: The finite volume method (CFD)	Written and oral assignments (part C) 1.5 credits	1), 2)
MTF073 Computational fluid dynamics: The finite volume method (CFD)	Examination 3 credits	S, 1), 2)
MVE095 Options and mathematics	Examination 7.5 credits	E, 1), 2)
TDA251 Algorithms, advanced course	Project 7.5 credits	E, 1), 2)
TDA507 Computational methods in bioinformatics	Written and oral assignments 7.5 credits	E, 1), 2)
TIF181 Science, innovation and entrepreneurship	Project 7.5 credits	E, 1), 2)

Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
FFR110 Computational biology	Examination 7.5 credits		E

Course code and name	Module, credits/period	Note
DAT675 Artificial intelligence for molecules	Written and oral assignments 3 credits
DAT675 Artificial intelligence for molecules	Project 4.5 credits	S
MVE155 Statistical inference	Examination 7.5 credits	E, 1)
MVE220 Financial risk	Examination 7.5 credits	E
TDA206 Discrete optimization	Examination 7.5 credits	E
TDA233 Algorithms for machine learning and inference	Written and oral assignments 3 credits
TDA233 Algorithms for machine learning and inference	Examination 4.5 credits	S
TIF150 Information theory for complex systems	Examination 7.5 credits	E, 1)
TIF320 Computational materials and molecular physics	Project 7.5 credits	E, 1)
TMA285 Financial derivatives and stochastic analysis	Examination 7.5 credits	E, 1)
TME286 Interpretable artificial intelligence	Project 7.5 credits	E, 1)

Course code and name	Module, credits/period	Note
MTF271 Turbulence modeling	Written and oral assignments (part A) 1.5 credits	1)
MTF271 Turbulence modeling	Written and oral assignments (part B) 1.5 credits	1)
MTF271 Turbulence modeling	Examination 4.5 credits	S, 1)
MVE166 Linear and integer optimization with applications	Examination (part A) 6 credits	1)
MVE166 Linear and integer optimization with applications	Project (part B) 1.5 credits	S, 1)
MVE441 Statistical learning for big data	Project 1.5 credits	1)
MVE441 Statistical learning for big data	Take-home examination 6 credits	S, 1)
TIF106 Non-equilibrium processes in physics, chemistry and biology	Examination 7.5 credits	E, 1)
TIF360 Advanced machine learning with neural networks	Project 7.5 credits	E, 1)
TME290 Autonomous robots	Examination 7.5 credits	E, 1)
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: - (EEN100, ENM140, FKA122, KMG060, MTF073, MTF271, MVE095, MVE155, MVE166, MVE441, RRY025, TDA251, TDA507, TIF106, TIF150, TIF160, TIF181, TIF295, TIF320, TIF360, TIF430, TIN093, TMA285, TMA883, TMA947, TME286, TME290, TMS016, TMS089, TMS165) 22.5 credits of stated courses are required for the degree
2 Recommendation: The course is normally followed during the second year of the Masters programme (EEN100, ENM140, FKA122, KMG060, MTF073, MVE095, RRY025, TDA251, TDA507, TIF160, TIF181, TIF295, TIF430, TIN093, TMA883, TMA947, TMS165)
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.
E: The only module in the course. Module grade and grade for the course are reported at the same time.
S: Final grade. All module grades are reported before the final grade for the course can be reported.