| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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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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| 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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Period 2 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 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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|
|
|
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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Period 2 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 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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