Compulsory study components
| Code | Study component | Quartile | Credits |
|---|---|---|---|
| 2AMC05 | Graduation Preparation | year | 10 |
| 2AMC00 | Graduation Project | year | 30 |
| 2IMR10 | Study and Career Orientation1 | year | 0 |
Trajectories
DS&AI in Context
| Code | Study component | Quartile | Credits | |
|---|---|---|---|---|
| 0LM190 | C | Philosophy and Ethics of AI | 2 | 5 |
| 2AMC15 | C | Data Intelligence Challenge | 4 | 5 |
| 2IMP40 | Empirical Methods in Software Engineering | 2 | 5 |
Data Engineering & Management
| Code | Study component | Quartile | Credits | |
|---|---|---|---|---|
| 2AMD15 | C | Big Data Management | 3 | 5 |
| 2IMS25 | Principles of Data Protection | 1 | 5 | |
| 2IMD10 | Engineering Data Systems | 2 | 5 | |
| 2AMD20 | Knowledge Engineering | 4 | 5 |
Algorithmic Data Analysis
| Code | Study component | Quartile | Credits | |
|---|---|---|---|---|
| 2IMA20 | Algorithms for Geovisualization | 3 | 5 | |
| 2AMS50 | Optimization for Data Science3 | 3 | 5 | |
| 2IMA30 | Topological Data Analysis | 4 | 5 |
Statistics
| Code | Study component | Quartile | Credits | |
|---|---|---|---|---|
| 2AMS10 | C | Longitudinal Data Analysis6 | 1 | 5 |
| 2AMS30 | Network Statistics for Data Science3 | 1 | 5 | |
| 2DI70 | Statistical Learning Theory | 3 | 5 | |
| 2DD23 | Time Series Analysis and Forecasting | 3 | 5 |
Process Mining & Visual Analytics (Explainable Data Analytics)
| Code | Study component | Quartile | Credits | |
|---|---|---|---|---|
| 2AMI10 | E | Foundations of Process Mining | 1 | 5 |
| 2AMI20 | Advanced Process Mining | 2 | 5 | |
| 2AMV10 | E | Visual Analytics | 4 | 5 |
Data Mining & Machine Learning
| Code | Study component | Quartile | Credits | |
|---|---|---|---|---|
| 2AMM20 | E | Research Topics in Data Mining | 1 | 5 |
| 2AMM30 | Text Mining3 | 1 | 5 | |
| 2AMS40 | Learning Optimal Decision Strategies & Reinforcement Learning3 | 2 | 5 | |
| 2AMM15 | Machine Learning Engineering | 3 | 5 | |
| 2AMM10 | Deep Learning | 4 | 5 |
AI & Machine Learning
| Code | Study component | Quartile | Credits | |
|---|---|---|---|---|
| 2AMU20 | Generative AI models | 4 | 5 | |
| 2AMU10 | C | Foundations of AI | 2 | 5 |
| 2AMU30 | Uncertainty Representation and Reasoning | 1 | 5 | |
| 2AMM40 | Advanced Topics in AI3 | 1 | 5 |
Notes to the tables:
- C: core courses
- E: core electives
- Other: specialization elective courses2
Seminar
Students can pick 1 from this list4.
| Code | Study component | Quartile | Credits | |
|---|---|---|---|---|
| 2IMI00 | Seminar Process Analytics | 1 | 5 | |
| 2IMV00 | Seminar Visualization | 1 | 5 | |
| 2IMD00 | Seminar Data Management | 2 | 5 | |
| 2IMU00 | Seminar Uncertainty in AI | 2 | 5 | |
| 2IMM00 | Seminar Data Mining | 2 | 5 | |
| 2AMS00 | Seminar SPOR | 1 | 5 | |
| 2IMN00 | Seminar IRIS | 2, 4 | 5 | |
| 2IMP00 | Seminar SET | 2, 4 | 5 | |
| 2IMF00 | Seminar Formal System Analysis | 2, 4 | 5 | |
| 2IMS00 | Seminar IST | 4 | 5 | |
| 2IMA00 | Seminar Algorithm | 4 | 5 |
Electives
All study components at master level offered at TU/e can be chosen as free electives, including further courses in any of the trajectories5.
Notes
1 This study component must be completed as compulsory part of study component AMC05. For students starting their studies prior to academic year 2023-2024 this concerns the administrative part only.
2 Students follow all 5 core courses (indicated with a C in the table), choose at least 1 core elective (indicated with an E in the table). In addition, students choose 2 trajectories as "major" trajectories and follow 2 courses in each major trajectory. Next to that, students pick 2 minor elective courses that are not in their major trajectories.
3 Course with capacity limit.
4 1 seminar from this list. A seminar may be followed starting from the fourth quartile of the program.
5 All study components at master level offered at TU/e can be chosen as free electives, including further courses in any of the trajectories.
6 This course will be offered for the last time in academic year 2023-2024.