Graduate university study programme in mathematics (Master level) - Mathematics and Computer Science
Study programme
Academic title: Master of Mathematics (MMath)
Year I
| Code | Course | L+E+S ~ ECTS | |
|---|---|---|---|
| Winter | Summer | ||
| MI009 | Applied linear algebra and scientific computing | 4+2+1 ~ 9 | |
| M118 | Probability | 4+3+0 ~ 9 | |
| I066 | Intelligent Robotic Systems | 3+2+1 ~ 8 | |
| MI008 | Semantics of programming languages | 2+2+0 ~ 6 | Total | 13 ~ 17 | 11 ~ 15 |
In order to earn the mandatory academic year study workload of 60 ECTS credits, students shall choose elective courses from the list below.
Criteria for enrolment in Year 2: a minimum of 45 ECTS credits which must obligatorily include ECTS credits allocated to the course Intelligent Robotic Systems.
Year II
| Code | Course | L+E+S ~ ECTS | |
|---|---|---|---|
| Winter | Summer | ||
| MIP001 | Master Thesis | 0+0+0 ~ 8 | |
| M128 | Linear optimization | 3+2+1 ~ 8 | |
| I067 | Operating systems | 3+2+1 ~ 8 | |
| I068 | Advanced programming techniques | 2+2+1 ~ 7 | |
| M129 | Nonlinear optimization | 3+2+0 ~ 7 | Total | 12 ~ 16 | 10 ~ 22 |
To achieve at least 60 ECTS for the academic year, student must take elective courses from the List of elective courses.
Elective courses:
MR - A: recommended for students who want to focus on computer science
| Code | Course | L+E+S ~ ECTS |
|---|---|---|
| M124 | Advanced concepts in machine learning | 3+2+1 ~ 8 |
| I069 | Natural Language Processing with Deep Learning | 3+2+1 ~ 8 |
| I071 | Approximation algorithms | 2+2+0 ~ 6 |
| I074 | Distributed Systems | 2+1+1 ~ 6 |
| I072 | Heuristic algorithms | 2+2+0 ~ 6 |
| MI006 | Cryptography | 2+2+0 ~ 6 |
| I033 | Parallel Programming | 2+2+0 ~ 6 |
| I075 | Compiler construction | 2+1+1 ~ 6 |
| I070 | Computer Vision | 2+2+0 ~ 6 |
| I065 | Randomized algorithms | 2+2+0 ~ 6 |
| I073 | Static program analysis | 2+1+1 ~ 6 |
| I064 | Contemporary topics in computer science | 2+2+0 ~ 6 |
| I076 | Linux operating system | 1+1+0 ~ 3 |
MR - B: recommended for students who want to focus on data science
| Code | Course | L+E+S ~ ECTS |
|---|---|---|
| M122 | Multivariate analysis | 2+1+1 ~ 7 |
| M003 | Time Series | 2+0+2 ~ 6 |
| M119 | Stochastic Processes I | 2+2+0 ~ 6 |
| M121 | Stochastic Processes II | 2+2+0 ~ 6 |
| M095 | Statistical Practice | 1+2+1 ~ 6 |
MR - C: recommended for students who want to focus on applied mathematics
| Code | Course | L+E+S ~ ECTS |
|---|---|---|
| M133 | Partial differential equations | 4+2+0 ~ 8 |
| M130 | Control Theory | 3+2+1 ~ 8 |
| M135 | Continuum mechanics | 3+2+0 ~ 7 |
| M134 | Numerical Methods for Partial Differential Equations | 3+2+0 ~ 7 |
| M131 | Dynamic systems | 2+2+0 ~ 6 |
| M136 | Fourier analysis and applications | 2+2+0 ~ 6 |
| MI001 | Graphs and Applications | 2+2+0 ~ 6 |
| MI007 | Complex Networks | 2+2+0 ~ 6 |
| M111 | Normed spaces | 2+2+0 ~ 6 |
| M132 | Model reduction and approximation methods | 2+1+1 ~ 6 |
| MI002 | Data Clustering and Applications | 2+1+1 ~ 5 |
| M048 | Decision Theory | 1+0+1 ~ 4 |
| M109 | Convex Functions | 1+1+0 ~ 3 |
| MI005 | Mathematical aspects of electoral systems | 1+0+1 ~ 3 |
MR - D
| Code | Course | L+E+S ~ ECTS |
|---|---|---|
| I040 | Project Management Basics | 1+1+0 ~ 4 |
| Z013 | Internship | 0+0+2 ~ 4 |