Course Manual MLWR
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 |
|---|---|
| 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 |
Literature
Final exam
| 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. |
|---|---|
| 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 |
Learning goals
| Goal type | Description |
|---|---|
| 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 |
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Lecture | 2 |
| Exercises (whole course) | 2 |
| Exercises (shared course) | 0 |
| Tutorial (voluntary) | 0 |
Special literature
| 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 |
Special requirements
| - |
| Accompanying material |
Lecture slides (electronic) possibly tutorials, instructional videos or links to them Practical task, partly with data sets and literature |
|---|---|
| Separate exam | No |
Learning goals
| Goal type | Description |
|---|---|
| Skills | Apply and program methods of approximation, multicriteria optimization or machine learning efficiently implement numerical methods Evaluate the complexity of algorithms |
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Practical training | 1 |
| Tutorial (voluntary) | 0 |
Special requirements
| - |
| Accompanying material |
Electronic task description sample programs Electronic tutorials for self-study |
|---|---|
| Separate exam | No |
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