Machine Learning and Scientific Computing
PDF Course Catalog Deutsche Version: MLWR
Version: 1 | Last Change: 27.09.2019 16:03 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben
Long name | Machine Learning and Scientific Computing |
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Approving CModule | MLWR_MaCSN, MLWR_MaET, MLWR_MaTIN |
Responsible |
Prof. Dr. Beate Rhein
Professor Fakultät IME |
Valid from | summer semester 2021 |
Level | Master |
Semester in the year | summer semester |
Duration | Semester |
Hours in self-study | 60 |
ECTS | 5 |
Professors |
Prof. Dr. Beate Rhein
Professor Fakultät IME |
Requirements | Basic knowledge of probability theory and machine learning |
Language | German |
Separate final exam | Yes |
Details |
Questions of different degrees of difficulty and different aspects of the course (course of a project, performance measures, data protection, etc.) some in-depth questions It is possible to write down sketches and formulas. |
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Minimum standard | be able to describe the rough sequence of a machine learning or scientific computing project Being able to explain discussed procedures roughly |
Exam Type | EN mündliche Prüfung, strukturierte Befragung |
Goal type | Description |
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Knowledge | Approximation methods metamodeling regression Multi-criteria optimization formulation Pareto front algorithms visualization Advanced Cluster Analysis Association Pattern Mining Outlier Detection Advanced classification procedures possibly text recognition, web mining, time series analysis |
Skills | Be familiar with mathematical methods, which are suitable for application tasks, convert them into run-time and memory optimized programs using numerical methods and skilful implementation Know approximation methods and select and apply the appropriate method for a task Formulate an application task as a multi-criteria optimization task and solve it in a program Know methods of machine learning, select and apply appropriate procedures |
Type | Attendance (h/Wk.) |
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Lecture | 2 |
Exercises (whole course) | 2 |
Exercises (shared course) | 0 |
Tutorial (voluntary) | 0 |
Data Mining - The Textbook, C.C. Aggarwal, Springer Verlag, ISBN 978-3-319-14141-1 |
Strukturoptimierung, L. Harzheim, Harri Deutsch Verlag, ISBN 978-3-8085-5659-7 |
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Accompanying material |
Lecture slides (electronic) possibly tutorials, instructional videos or links to them Practical task, partly with data sets and literature |
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Separate exam | No |
Goal type | Description |
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Skills | Apply and program methods of approximation, multicriteria optimization or machine learning efficiently implement numerical methods Evaluate the complexity of algorithms |
Type | Attendance (h/Wk.) |
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Practical training | 1 |
Tutorial (voluntary) | 0 |
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Accompanying material |
Electronic task description sample programs Electronic tutorials for self-study |
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Separate exam | No |
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