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Mandatory sequentiality bound on the level of programme components
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Following programme components must have been included in your study programme in a previous education period
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Probability theory and statistics (2941)
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6.0 stptn |
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There is no data for this choice.
Change the language, year or choose another item in the dropdown list if it is available.
There is no data for this choice.
Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | Master Computer Science profile Artificial Intelligence | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- EC
| EC 1: A graduate of the Master of Computer Science programme has insight into the most important technological developments in the field of computer science and the underlying scientific principles. | - EC
| EC 5: A graduate of the Master of Computer Science programme is able to independently model a complex problem in computer science, to introduce the necessary abstractions, to describe and to implement the solution in a structured manner, and, finally, to discuss with the stakeholders why the chosen solution and the corresponding implementation meet with the specifications. | - EC
| EC 6: A graduate of the Master of Computer Science programme is able to independently situate a scientific problem, analyse and evaluate it, to formulate a research question and propose a solution for this in a scientifically substantiated manner. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student can program fluently.
The studen has basic knowledge of statistics and linear algebra.
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The course gives an overview of different techniques within machine learning: methods for regression, classification including neural networks, and reinforcement learning.
Students acquire basic knowledge about the theoretical background of the techniques and are able to apply them practically in mini-projects where they can explain and present the acquired results.
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Lecture ✔
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Practical ✔
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Project ✔
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Self-study assignment ✔
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Exercises ✔
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Presentation ✔
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Report ✔
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Semester 2 (6,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 50 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory course material |
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Course slides and copies of articles.
The course material is distributed during the class or made available via the web (Blackboard) .
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| Master of Computer Science choice | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| EC 1: A graduate of the Master of Computer Science programme has insight into the most important technological developments in the field of computer science and the underlying scientific principles. | - EC
| EC 5: A graduate of the Master of Computer Science programme is able to independently model a complex problem in computer science, to introduce the necessary abstractions, to describe and to implement the solution in a structured manner, and, finally, to discuss with the stakeholders why the chosen solution and the corresponding implementation meet with the specifications. | - EC
| EC 6: A graduate of the Master of Computer Science programme is able to independently situate a scientific problem, analyse and evaluate it, to formulate a research question and propose a solution for this in a scientifically substantiated manner. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student can program fluently.
The studen has basic knowledge of statistics and linear algebra.
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|
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|
The course gives an overview of different techniques within machine learning: methods for regression, classification including neural networks, and reinforcement learning.
Students acquire basic knowledge about the theoretical background of the techniques and are able to apply them practically in mini-projects where they can explain and present the acquired results.
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Lecture ✔
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Practical ✔
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Project ✔
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Self-study assignment ✔
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Exercises ✔
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Presentation ✔
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Report ✔
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Semester 2 (6,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 50 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory course material |
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Course slides and copies of articles.
The course material is distributed during the class or made available via the web (Blackboard) .
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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