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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:
  • Lecture Advanced Topics in Machine Learning
  • Exercise class Advanced Topics in Machine Learning
 
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.

  • 2 SWS lecture
  • 2 SWS exercise class
Prerequisites according to examination regulations: Recommended prerequisites:

none

Module Machine Learning (ML) or Grundlagen des Maschinellen Lernens (GdML)

Media: Literature:


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