Advanced Topics in Machine Learning
(in German: Advanced Topics in Machine Learning )
Module-ID: FIN-INF-120265 |
| Link: | LSF |
| Responsibility: | Andreas Nürnberger |
| Lecturer: | Andreas Nürnberger |
| Classes: |
|
| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science |
|
Abbreviation ATiML |
Credit Points 6 |
Semester Summer |
Term ab 1. |
Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
Students who complete the course ...
- can understand and work with kernel models like Support Vector Machines
- can combine supervised, unsupervised and semi-supervised machine learning models to solve practical problems
- know the practical aspects of exploratory data analysis, feature engineering, evaluation of machine learning pipeline
- can use Python SK-learn and related machine learning toolkits
Content:
- Exploratory data analytics with data pipelines and evaluation
- Kernel-based methods and detailed exploration of support vector machines
- Semi-supervised methods, including graphical, kernel-based and meta-heuristic models
- Constrained clustering with instance-based and metric-based approaches
- Efficient data structures for handling multi-dimensional data such as images and text
Workload:
56 contact hours + 124h self study
| Pre-examination requirements: | Type of examination: | Teaching method / lecture hours per week (SWS): |
|
Written exam. Requirements for exam participation will be announced in the first week of the course in class and online. |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
none |
Module Machine Learning (ML) or Grundlagen des Maschinellen Lernens (GdML) |
| Media: | Literature: |
|
|
Comments: