Advanced Topics in Data Science DL (4585) |
| Credits: 3,0 | | Study load hours: 81 | Period: semester 1 (3sp)  |
| Language of instruction: English | | Exam contract: not possible |
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The student has basic knowledge in programming. The student has basic knowledge of linear algebra such as eigenvalue problems and singular value decomposition.
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Five different topics by five different professors: Distributed Data Analysis, Frequent Itemset Handling, Gaussian Processes and Bayesian Optimization, Dimensionality Reduction, Numerical Linear Algebra.
Each of these topics will have a task exam and a lab work, which will have an equal weight. A minimum score of 5/20 is needed for each topic. A short oral assessment will be part of the evaluation.
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| Compulsory course material |
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Different texts from the various modules. |
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| Recommended course material |
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Course material will be provided via blackboard |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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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 critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student is capable of acquiring new knowledge. |
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| Included in these programmes | Tolerance3 |
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second year Data Science - distance learning
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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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