Curriculum for Mathematical Modelling and Computation

Programme provision

To obtain the MSc degree in Mathematical Modelling and Computation the student must fulfill the following requirements: 

  • Have passed Polytechnical foundation courses adding up to at least 10 ECTS
  • Have passed Programme-specific courses adding up to at least 50 ECTS
  • Have performed a Master Thesis of 30 ECTS points within the field of the general program
  • Have passed a sufficient number of Elective courses to bring the total number of ECTS of the entire study to 120 ECTS

Curriculum

Polytechnical foundation courses (10 ECTS)

The following courses are mandatory:

12100 Quantitative Sustainability (Polytechnical Foundation) 5 point F7 (Tues 18-22)
or
12106 Quantitative Sustainability (Polytechnical Foundation) 5 point Autumn E3B (Fri 13-17)
or
12105 Quantitative Sustainability (Polytechnical Foundation) 5 point E7 (Tues 18-22)
or
12101 Quantitative Sustainability (Polytechnical Foundation) 5 point Spring F3B (Fri 13-17)
42500 Innovation in Engineering (Polytechnical Foundation) 5 point January
or
42504 Innovation in Engineering (Polytechnical Foundation) 5 point August
or
42501 Innovation in Engineering (Polytechnical Foundation) 5 point June

Students with advanced innovation competences may take one of the following courses as an alternative to 42500/42501/42504:

42502 Facilitating Innovation in Multidisciplinary Teams 5 point January
or
42505 Facilitating Innovation in Multidisciplinary Teams 5 point August
or
42503 Facilitating Innovation in Multidisciplinary Teams 5 point June

Programme specific courses (50 ECTS)

Innovation course II - choose 5 ECTS among the following courses  - if more than 5 ECTS are chosen they will count as electives: 

02492 Digital Learning Technology and Entrepreneurship 5 point Autumn E3A (Tues 8-12)
02809 UX Design Prototyping 5 point Autumn E1A (Mon 8-12)
38102 Technology Entrepreneurship 5 point Autumn E1B (Thurs 13-17)
38103 X-Tech Entrepreneurship 10 point Spring F3 (Tues 8-12, Fri 13-17), Autumn E3 (Tues 8-12, Fri 13-17)
38106 Developing an Entrepreneurial mindset 5 point Spring F1B (Thurs 13-17), Autumn E1B (Thurs 13-17)
42576 From Analytics to Action 5 point Spring F1A (Mon 8-12)

Core competence courses - mandatory (10 ECTS)

01617 Introduction to Dynamical Systems 5 point Autumn E3B (Fri 13-17)
02613 Python and High-Performance Computing 5 point Spring F5A (Wed 8-12)

Core competence courses - choose 5 ECTS from each of the following two groups of courses:

Optimization

02428 Dynamic Optimization 5 point Autumn E4A (Tues 13-17)
02611 Optimization for Data Science 5 point Spring F5B (Wed 13-17)
02612 Constrained Optimization 5 point Spring F4A (Tues 13-17)
42114 Integer Programming 5 point Autumn E4A (Tues 13-17)

Please note that 42114 Integer Programming is a mandatory prerequisite for some of the more advanced courses on operations research.

Statistics

02409 Multivariate Statistics 5 point Autumn E1A (Mon 8-12)
02443 Stochastic Simulation 5 point June
02582 Computational Data Analysis 5 point Spring F2B (Thurs 8-12)

Choose 25 ECTS among the rest of the programme-specific courses: 

01238 Differential Geometry 5 point Spring F2B (Thurs 8-12)
01257 Advanced Modelling - Applied Mathematics 5 point January
01325 Mathematics 4: Analysis - a Toolbox in Physics and Engineering 5 point Spring F1A (Mon 8-12)
01405 Algebraic Error-Correcting Codes 5 point Spring F4B (Fri 8-12)
01415 Computational Discrete Mathematics 5 point Autumn E5B (Wed 13-17)
01426 Cryptography 2 5 point Autumn E3A (Tues 8-12)
01617 Introduction to Dynamical Systems 5 point Autumn E3B (Fri 13-17)
01621 Advanced Dynamical Systems: Global Theory 5 point Spring F1A (Mon 8-12)
01622 Advanced Dynamical Systems: Applications in Science and Engineering 5 point Spring F3B (Fri 13-17)
01715 Functional Analysis 5 point Autumn E4B (Fri 8-12)
02232 Applied Cryptography 5 point Autumn E1B (Thurs 13-17)
02246 Model Checking 7.5 point Autumn E4B (Fri 8-12)
02249 Computationally Hard Problems 7.5 point Autumn E3A (Tues 8-12)
02255 Modern Cryptography 5 point Spring F5B (Wed 13-17)
02407 Stochastic Processes - Probability 2 5 point Autumn E3A (Tues 8-12)
02409 Multivariate Statistics 5 point Autumn E1A (Mon 8-12)
02411 Statistical Design and Analysis of Experiments 5 point Autumn E2A (Mon 13-17), Spring F1A (Mon 8-12)
02417 Time Series Analysis 5 point Spring F4B (Fri 8-12)
02421 Stochastic Adaptive Control 5 point Spring F3A (Tues 8-12)
02425 Diffusions and stochastic differential equations 5 point Autumn E1B (Thurs 13-17)
02426 Non-linear random effect models: time-independent and dynamic models 5 point Spring F2A (Mon 13-17)
02427 Advanced Time Series Analysis 10 point Autumn E5 (Wed 8-17)
02429 Analysis of correlated data: Mixed linear models 5 point Autumn E2B (Thurs 8-12)
02431 Risk Management 5 point January
02443 Stochastic Simulation 5 point June
02456 Deep learning 5 point Autumn E2A (Mon 13-17)
02458 Cognitive Modelling 5 point Autumn E2B (Thurs 8-12)
02460 Advanced Machine Learning 5 point Spring F1B (Thurs 13-17)
02471 Machine learning for signal processing 5 point Autumn E1B (Thurs 13-17)
02477 Bayesian machine learning 5 point Spring F2A (Mon 13-17)
02492 Digital Learning Technology and Entrepreneurship 5 point Autumn E3A (Tues 8-12)
02501 Advanced Deep Learning in Computer Vision 5 point Spring F4A (Tues 13-17)
02506 Advanced Image Analysis 5 point Spring F5B (Wed 13-17)
02516 Introduction to Deep Learning in Computer Vision 5 point Autumn E5B (Wed 13-17)
02561 Computer Graphics 5 point Autumn E5A (Wed 8-12)
02562 Rendering - Introduction 5 point Autumn E5B (Wed 13-17)
02563 Generative Methods for Computer Graphics 5 point Spring F1B (Thurs 13-17)
02582 Computational Data Analysis 5 point Spring F2B (Thurs 8-12)
02612 Constrained Optimization 5 point Spring F4A (Tues 13-17)
02614 High-Performance Computing 5 point January
02616 Large-scale Modelling 5 point Spring F3A (Tues 8-12)
02619 Model Predictive Control 5 point Autumn E2B (Thurs 8-12)
02623 The Finite Element Method for Partial Differential Equations 5 point January
02624 Inverse problems and Imaging 5 point Autumn E5B (Wed 13-17)
02686 Scientific computing for differential equations 5 point Spring F1B (Thurs 13-17)
02687 Scientific Computing for ordinary and partial differential equations 5 point Spring F1A (Mon 8-12)
02807 Computational Tools for Data Science 5 point E7 (Tues 18-22)
42112 Mathematical Programming Modelling 5 point January
42114 Integer Programming 5 point Autumn E4A (Tues 13-17)
42115 Network Optimization 5 point Autumn E4B (Fri 8-12)
42136 Large Scale Optimization using Decomposition 5 point Spring F2B (Thurs 8-12)
42137 Optimization using metaheuristics 5 point Spring F2A (Mon 13-17)
42186 Model-based machine learning 5 point Spring F5B (Wed 13-17)

Elective Courses

Any course classified as an MSc course in DTU's course base may be an elective course. This includes programme-specific courses above the minimal requirements. Master students may choose as many as 10 ECTS among the bachelor courses at DTU and courses at an equivalent level from other higher institutions. In addition, it is possible to take MSc-level courses at other Danish universities or abroad.

Head of studies