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.

2018W, VU, 3.0h, 4.5EC
TUWEL

Merkmale

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

Ziele der Lehrveranstaltung

Attention: after the winter term 2018/2019, this lecture will be moved to the summer term. The first lecture cycle in summer term is in summer term 2019, the following edition will be summer term 2020. 

Principles of Supervised Machine Learning, including algorithms, meta-algorithms, evaluation, ...

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: 3.10. 2018

Weitere Informationen

Attention: after the winter term 2018/2019, this lecture will be moved to the summer term. The first lecture cycle in summer term is in summer term 2019, the following edition will be summer term 2020. 

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 Personen

Institut

LVA Termine

TagZeitDatumOrtBeschreibung
Mi.16:00 - 18:0003.10.2018 - 30.01.2019EI 8 Pötzl HS - QUER Vorlesung
Mi.10:00 - 13:0028.11.2018Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Do.15:00 - 17:3029.11.2018Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Fr.12:00 - 15:0030.11.2018Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Di.15:00 - 18:0008.01.2019Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 2
Mi.10:00 - 13:0009.01.2019Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 2
Mi.11:00 - 13:0023.01.2019Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 3
Mi.14:00 - 18:0023.01.2019Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 3
Mi.12:30 - 17:0030.01.2019Seminarraum FAV EG B (Seminarraum von Neumann) Presentations Exercise 3
Do.12:00 - 17:0031.01.2019FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 3
Machine Learning - Einzeltermine
TagDatumZeitOrtBeschreibung
Mi.03.10.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.10.10.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.17.10.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.24.10.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.31.10.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.07.11.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.14.11.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.21.11.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.28.11.201810:00 - 13:00Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Mi.28.11.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Do.29.11.201815:00 - 17:30Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Fr.30.11.201812:00 - 15:00Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Mi.05.12.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.12.12.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.19.12.201816:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Di.08.01.201915:00 - 18:00Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 2
Mi.09.01.201910:00 - 13:00Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 2
Mi.09.01.201916:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.16.01.201916:00 - 18:00EI 8 Pötzl HS - QUER Vorlesung
Mi.23.01.201911:00 - 13:00Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 3

Leistungsnachweis

Übungsbeispiele und schriftliche Prüfung

Prüfungen

TagZeitDatumOrtPrüfungsmodusAnmeldefristAnmeldungPrüfung
Do.13:00 - 15:0007.03.2024EI 7 Hörsaal - ETIT beurteilt07.02.2024 00:00 - 04.03.2024 23:59in TISSExam (WS2023 1st re-take)
Di.15:00 - 17:0030.04.2024Informatikhörsaal - ARCH-INF beurteilt29.03.2024 23:00 - 25.04.2024 23:59in TISSExam (WS2023 2nd & final re-take)
Mi. - 26.06.2024schriftlich27.05.2024 00:00 - 23.06.2024 23:59in TISSExam (2024 main date)

LVA-Anmeldung

Von Bis Abmeldung bis
01.08.2018 00:00 17.10.2018 23:59 20.10.2018 23:59

Curricula

StudienkennzahlVerbindlichkeitSemesterAnm.Bed.Info
066 011 DDP Computational Logic (Erasmus-Mundus) Keine Angabe
066 645 Data Science Keine Angabe
066 926 Business Informatics Gebundenes Wahlfach
066 931 Logic and Computation Gebundenes Wahlfach
066 937 Software Engineering & Internet Computing Gebundenes Wahlfach

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

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