Courses for Applied and Computational Mathematics
The two-year master's programme in Applied and Computational Mathematics consists of three terms of courses and one final term dedicated to the master's degree project. Each term consist of approximately 30 ECTS credits. Depending on which track you choose, you will study different courses. The courses presented on this page apply to studies starting in autumn 2022.
Year 1
At least one of the conditionally elective courses among the general courses has to be studied.
The list of recommended courses is those that we think you will need for your future career.
Mandatory courses for all tracks
Conditionally elective courses for all tracks
Recommended courses for all tracks
- Visualization (DD2257) 7.5 credits
- Methods in High Performance Computing (DD2356) 7.5 credits
- Advanced Computation in Fluid Mechanics (DD2365) 7.5 credits
- Machine Learning (DD2421) 7.5 credits
- Machine Learning, Advanced Course (DD2434) 7.5 credits
- Mathematical Modelling of Biological Systems (DD2435) 9.0 credits
- Optimization (SF1811) 6.0 credits
- Computational Methods for Stochastic Differential Equations and Machine Learning (SF2525) 7.5 credits
- Numerical algorithms for data-intensive science (SF2526) 7.5 credits
- Program Construction in C++ for Scientific Computing (SF2565) 7.5 credits
- Computational Fluid Dynamics (SG2212) 7.5 credits
- Applied Computational Fluid Dynamics (SG2224) 5.0 credits
Optional courses
- Numerical Solutions of Differential Equations (SF2521) 7.5 credits
- Matrix Computations for Large-scale Systems (SF2524) 7.5 credits
- The Finite Element Method (SF2561) 7.5 credits
- Project Course in Scientific Computing (SF2567) 7.5 credits
- Parallel Computations for Large- Scale Problems (SF2568) 7.5 credits
- Financial Mathematics, Basic Course (SF2701) 7.5 credits
- Applied Nonlinear Optimization (SF2822) 7.5 credits
- Geometric Control Theory (SF2842) 7.5 credits
- Optimal Control Theory (SF2852) 7.5 credits
- Applied Systems Engineering (SF2866) 7.5 credits
- Regression Analysis (SF2930) 7.5 credits
- Modern Methods of Statistical Learning (SF2935) 7.5 credits
- Portfolio Theory and Risk Management (SF2942) 7.5 credits
- Time Series Analysis (SF2943) 7.5 credits
- Computer Intensive Methods in Mathematical Statistics (SF2955) 7.5 credits
- Topological Data Analysis (SF2956) 7.5 credits
- Martingales and Stochastic Integrals (SF2971) 7.5 credits
- Financial Derivatives (SF2975) 7.5 credits
- Risk Management (SF2980) 7.5 credits
Year 2
At least one of the conditionally elective courses among the general courses has to be studied.
The list of recommended courses is those that we think you will need for your future career.
Conditionally elective courses for all tracks
Recommended courses for all tracks
- Visualization (DD2257) 7.5 credits
- Machine Learning (DD2421) 7.5 credits
- Machine Learning, Advanced Course (DD2434) 7.5 credits
- Mathematical Modelling of Biological Systems (DD2435) 9.0 credits
- Optimization (SF1811) 6.0 credits
- Program Construction in C++ for Scientific Computing (SF2565) 7.5 credits
Optional courses
- Matrix Computations for Large-scale Systems (SF2524) 7.5 credits
- The Finite Element Method (SF2561) 7.5 credits
- Project Course in Scientific Computing (SF2567) 7.5 credits
- Optimal Control Theory (SF2852) 7.5 credits
- Applied Systems Engineering (SF2866) 7.5 credits
- Modern Methods of Statistical Learning (SF2935) 7.5 credits
- Portfolio Theory and Risk Management (SF2942) 7.5 credits
- Topological Data Analysis (SF2956) 7.5 credits
- Statistical Machine Learning (SF2957) 7.5 credits
- Financial Derivatives (SF2975) 7.5 credits
- Risk Management (SF2980) 7.5 credits