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184.702 Machine Learning
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2020S, VU, 3.0h, 4.5EC

Merkmale

  • Semesterwochenstunden: 3.0
  • ECTS: 4.5
  • Typ: VU Vorlesung mit Übung

Lernergebnisse

Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage..

- Formulate problems as specific Machine Learning tasks

- Understand of a range of machine learning algorithms and their characteristics

- Select the fitting methods for a specific learning goal

- Explain data preprocessing techniques

- Evaluate the methods for their suitability

Inhalt der Lehrveranstaltung

Principles of Supervised and Unsupervised Machine Learning, including pre-processing and Data Preparation, as well as Evaluation of Learning Systems. Machine Learning models discussed may include e.g. Decision Tree Learning, Model Selection, Bayesian Networks, Regression techniques, Support Vector Machines, Random Forests as well as ensemble methods.

Didactical Concept:
-Lectures
-Exercises:
students will compare different machine algorithms for particular data sets, and have to implement a machine learning algorithm - Presentation of algorithms by students - Discussion of reports that summarize the comparison of machine learning algorithms

Assessment: is based on written exam, report, and implemented machine learning algorithms

Preliminary talk (Vorbesprechung) & intro: 3.3. 2020

Methoden

The course contains classroom lectures and exercises. Exercises include the application of machine learning techniques for various data sets and implementation of machine learning algorithms. The exercises are prepared at home and will be presented/discussed during the exercise classes. 

Prüfungsmodus

Prüfungsimmanent

Weitere Informationen

This course will be held in both summer and winter term from, summer semester 2019 on.

 

This course will be held completely in TUWEL - all lecture materials and news about the lecture will be made available there, and all questions regarding the course should be asked in the TUWEL forum *only*, not via TISS.

To get access to the TUWEL course, just apply to the group in TISS, and then follow the TUWEL link above

 

ECTS Breakdown:

8 classes (including prepration): 22 h

4 classes for presentations/discussions (including preparation): 12

Assignments: 46.5 h

exam: 32 h

---------------

total: 112.5 h

Vortragende

Institut

LVA Termine

TagZeitDatumOrtBeschreibung
Di.12:00 - 14:0003.03.2020 - 30.06.2020EI 8 Pötzl HS Lectures
Machine Learning - Einzeltermine
TagDatumZeitOrtBeschreibung
Di.03.03.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.10.03.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.17.03.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.24.03.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.31.03.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.21.04.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.28.04.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.05.05.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.12.05.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.19.05.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.26.05.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.09.06.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.16.06.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.23.06.202012:00 - 14:00EI 8 Pötzl HS Lectures
Di.30.06.202012:00 - 14:00EI 8 Pötzl HS Lectures

Leistungsnachweis

- Solving of exercises regarding experiments in machine learning, using a software toolkit of the student's choice (e.g. Python scikit-learn, Matlab, R, WEKA, ...): 50%

- Written exam at the end of the semester: 50%

Prüfungen

TagZeitDatumOrtPrüfungsmodusAnmeldefristAnmeldungPrüfung
Mi.17:00 - 19:0020.05.2020Informatikhörsaal beurteilt23.04.2020 00:00 - 18.05.2020 23:59in TISSExam - 2nd Retake
Do.11:00 - 13:0025.06.2020EI 7 Hörsaal schriftlich20.05.2020 00:00 - 23.06.2020 23:59in TISSExam (main date summer semester, last retake winter semester)

LVA-Anmeldung

Von Bis Abmeldung bis
11.12.2019 12:00 18.03.2020 23:59 18.03.2020 23:59

Curricula

Literatur

Es wird kein Skriptum zur Lehrveranstaltung angeboten.

Vorkenntnisse

Self-Organising Systems (188.413) offers complementary topics in unsupervised data analysis. Information Retrieval (188.412) applies principles from Data Mining, Machine Learning

As a subsequent course, Problem Solving and Search in Artificial Intelligence (181.190) teaches some problem solving techniques that can be used in machine learning.

Begleitende Lehrveranstaltungen

Vertiefende Lehrveranstaltungen

Sprache

Englisch