| Language of instruction: English |
| | | Exam contract: not possible |
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Mandatory sequentiality bound on the level of programme components
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Group 1 |
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Following programme components must have been included in your study programme in a previous education period
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Concepts of Probability and Statistics DL (3220)
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5.0 stptn |
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Linear Models DL (3577)
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5.0 stptn |
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Programming in R DL (4432)
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3.0 stptn |
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Or group 2 |
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Following programme components must have been included in your study programme in a previous education period
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Concepts of Probability and Statistics DL (3220)
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5.0 stptn |
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Linear Models DL (3577)
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5.0 stptn |
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Programming in Python DL (3587)
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5.0 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 | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Master Bioinformatics - distance learning | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical |  |
| second year Data Science - distance learning | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student has knowledge about basic statistics and mathematics, such as, e.g., maximum likelihood estimation, linear regression, binary classification, matrix algebra, optimization. The student can program in R or Python.
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In this coursework we give a non-exhaustive overview of the basic principles of machine learning. In several classes we cover topics like, bias/variance trade off, simple linear regression and classification, cross validation and bootstrapping, unsupervised methods, feature selection methods, splines, random forests, and support vector machines. The theory is applied on a Kaggle competition. The course aims at junior level data scientists. Notion of programming and mathematics/statistics is mandatory.
- A framework for machine learning
- Simple supervised methods: Linear regression and Classification
- Resampling methods, Model selection and Regularization
- Moving beyond simple supervised methods:
- Regression splines
- Local regression
- Generalized additive models
- Tree-based method
- Support Vector Machines
- Unsupervised techniques
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Flipped classroom ✔
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Project ✔
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Self-study assignment ✔
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Semester 1 (5,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 25 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | The student needs to pass this component of evaluation. |
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| Oral evaluation during teaching period | 25 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | The student needs to pass this component of evaluation. |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | A student must at least attend all components of the evaluation.
A student must obtain a tolerable exam result (≥8/20) for each component to be able to pass the programme component. |
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| Consequences | If a student does not attend one of the evaluation components, he/she will receive an 'X' for the programme component.
If the student achieves less than 8/20 in part of this programme component, this lowest partial grade will be the final grade for the entire programme component for the examination opportunity concerned. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory textbooks (bookshop) |
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[An Introduction to Statistical Learning with Applications in R],[James, G., Witten, D., Hastie, T. and Tibshirani, R.],[2013],[Springer-Verlag],[],[(e-copy freely available online)] |
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| Compulsory course material |
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Lecture notes will be made available at Blackboard. |
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| Recommended reading |
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[The Elements of Statistical Learning],[Hastie, T., Tibshirani, R. and Friedman, J.],[2009],[Springer-Verlag],[],[Available as e-book: https://link.springer.com/book/10.1007%2F978-0-387-84858-7] |
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| second year Master Biostatistics - distance learning | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| second year Quantitative Epidemiology - distance learning | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The student has knowledge about basic statistics and mathematics, such as, e.g., maximum likelihood estimation, linear regression, binary classification, matrix algebra, optimization. The student can program in R or Python.
|
|
|
|
|
In this coursework we give a non-exhaustive overview of the basic principles of machine learning. In several classes we cover topics like, bias/variance trade off, simple linear regression and classification, cross validation and bootstrapping, unsupervised methods, feature selection methods, splines, random forests, and support vector machines. The theory is applied on a Kaggle competition. The course aims at junior level data scientists. Notion of programming and mathematics/statistics is mandatory.
- A framework for machine learning
- Simple supervised methods: Linear regression and Classification
- Resampling methods, Model selection and Regularization
- Moving beyond simple supervised methods:
- Regression splines
- Local regression
- Generalized additive models
- Tree-based method
- Support Vector Machines
- Unsupervised techniques
|
|
|
|
|
|
|
|
|
Flipped classroom ✔
|
|
|
|
Project ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Semester 1 (5,00sp)
| Evaluation method | |
|
| Written evaluation during teaching period | 25 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
| Conditions transfer of partial marks within the academic year | The student needs to pass this component of evaluation. |
|
|
|
|
|
|
|
|
|
| Oral evaluation during teaching period | 25 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
| Conditions transfer of partial marks within the academic year | The student needs to pass this component of evaluation. |
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|
|
|
|
|
|
|
|
|
| Off campus online evaluation/exam | ✔ |
|
| For the full evaluation/exam | ✔ |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | A student must at least attend all components of the evaluation.
A student must obtain a tolerable exam result (≥8/20) for each component to be able to pass the programme component. |
|
|
|
| Consequences | If a student does not attend one of the evaluation components, he/she will receive an 'X' for the programme component.
If the student achieves less than 8/20 in part of this programme component, this lowest partial grade will be the final grade for the entire programme component for the examination opportunity concerned. |
|
|
|
|
| Compulsory textbooks (bookshop) |
| |
[An Introduction to Statistical Learning with Applications in R],[James, G., Witten, D., Hastie, T. and Tibshirani, R.],[2013],[Springer-Verlag],[],[(e-copy freely available online)] |
|
|
| Compulsory course material |
| |
Lecture notes will be made available at Blackboard. |
|
|
| Recommended reading |
| |
[The Elements of Statistical Learning],[Hastie, T., Tibshirani, R. and Friedman, J.],[2009],[Springer-Verlag],[],[Available as e-book: https://link.springer.com/book/10.1007%2F978-0-387-84858-7] |
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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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