| Credits: 6,0 | | Study load hours: 162 | Period: semester 2 (6sp)  |
| 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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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 (9767)
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6.0 stptn | |
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Linear Models (9770)
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7.0 stptn | |
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Programming in R (4406)
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3.0 stptn | |
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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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| Compulsory course material |
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An Introduction to Statistical Learning with Applications in R/Python, Available as e-book:https://www.statlearning.com/
Addit ional lecture notes will be made available at Blackboard. |
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| Recommended reading |
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Project ✔
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Self-study assignment ✔
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Tutorial group ✔
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Semester 2 (6,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 25 % |
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| Transfer of partial marks within the academic year | Yes, with condition |
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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 | Yes, with condition |
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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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| Written exam | 50 % |
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| Multiple-choice questions | ✔ |
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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 'N' 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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| Explanation (English) | Should the student fail to attain the minimum satisfactory threshold for
the collaborative project, the student must complete an individual
project. The administration of the written examination during the
second-attempt session shall mirror the procedures of the first-attempt
examination. |
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Learning outcomes | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
Master of Statistics and Data Science
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- 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. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student can put research and consulting aspects of one or more statistical fields into practice. | | | - DC
| The student can put the research aspects of one or more statistical fields into practice.
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| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.
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| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application. | - EC
| The student is able to efficiently acquire, store and process data. | | | - DC
| ... selecting and using the best data management options | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective oral communicator in their own field. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student routinely monitors his/her own learning process and adjusts and improves it accordingly. |
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| Included in these programmes | Tolerance3 |
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Y
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
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