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.

2017W, VU, 3.0h, 4.5EC
TUWEL

Merkmale

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

Ziele der Lehrveranstaltung

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, Support Vector Machines, Random Forests, Hidden Markov Models, 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

Weitere Informationen

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
Mi.15:30 - 18:0011.10.2017EI 8 Pötzl HS Vorlesung
Mi.16:00 - 18:0018.10.2017 - 24.01.2018EI 8 Pötzl HS Vorlesung
Mi.10:00 - 12:0029.11.2017Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Mi.16:00 - 18:0029.11.2017EI 8 Pötzl HS Vorlesung
Do.15:00 - 17:0030.11.2017Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Fr.12:00 - 15:0001.12.2017Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Mi.16:00 - 18:0013.12.2017HS 13 Ernst Melan - RPL Vorlesung (Ausweichtermin)
Mi.10:00 - 13:0020.12.2017Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 2
Do.14:00 - 19:0021.12.2017Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 2
Mi.16:00 - 18:0010.01.2018EI 9 Hlawka HS -MWB Vorlesung (Ausweichtermin)
Mi.11:00 - 13:0024.01.2018Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 3
Mi.14:00 - 18:0024.01.2018Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 3
Mi.12:30 - 17:0031.01.2018Seminarraum FAV EG B (Seminarraum von Neumann) Presentations Exercise 3
Do.12:00 - 17:0001.02.2018FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 3
Machine Learning - Einzeltermine
TagDatumZeitOrtBeschreibung
Mi.11.10.201715:30 - 18:00EI 8 Pötzl HS Vorlesung
Mi.18.10.201716:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.25.10.201716:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.08.11.201716:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.22.11.201716:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.29.11.201710:00 - 12:00Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Mi.29.11.201716:00 - 18:00EI 8 Pötzl HS Vorlesung
Do.30.11.201715:00 - 17:00Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Fr.01.12.201712:00 - 15:00Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 1
Mi.06.12.201716:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.13.12.201716:00 - 18:00HS 13 Ernst Melan - RPL Vorlesung (Ausweichtermin)
Mi.20.12.201710:00 - 13:00Seminarraum FAV 01 A (Seminarraum 183/2) Presentations Exercise 2
Mi.20.12.201716:00 - 18:00EI 8 Pötzl HS Vorlesung
Do.21.12.201714:00 - 19:00Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 2
Mi.10.01.201816:00 - 18:00EI 9 Hlawka HS -MWB Vorlesung (Ausweichtermin)
Mi.17.01.201816:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.24.01.201811:00 - 13:00Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 3
Mi.24.01.201814:00 - 18:00Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 3
Mi.24.01.201816:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.31.01.201812:30 - 17:00Seminarraum FAV EG B (Seminarraum von Neumann) Presentations Exercise 3

Leistungsnachweis

schriftliche Prüfung

Prüfungen

TagZeitDatumOrtPrüfungsmodusAnmeldefristAnmeldungPrüfung
Fr.16:00 - 18:3019.03.2021 beurteilt29.01.2021 12:00 - 12.03.2021 23:59in TISSExam (1st retake WS20, 5th and final retake SS20; date confirmed)
Mi. - 19.05.2021beurteilt22.04.2021 00:00 - 12.05.2021 23:59in TISSExam (2nd retake WS 20; date not yet confirmed, depends on the COVID situation!)
Do. - 24.06.2021Prechtlsaal großer Teil - Achtung! Werkraum, kein Hörsaal! schriftlich19.05.2021 00:00 - 21.06.2021 12:00in TISSExam (Main date SS21, 3rd retake WS20)

LVA-Anmeldung

Von Bis Abmeldung bis
07.02.2017 00:00 13.10.2017 12:00 15.10.2017 23:00

Gruppen-Anmeldung

GruppeAnmeldung VonBis
A30.08.2017 12:0013.10.2017 23:59

Curricula

Literatur

Es wird kein Skriptum zur Lehrveranstaltung angeboten.

Vorkenntnisse

Two chapters of Data Mining/Business Intelligence (188.429) courses will be posted in TUWEL. If you did not attend this course before, please read this material before the first class of Machine Learning course.

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

Begleitende Lehrveranstaltungen

Vertiefende Lehrveranstaltungen

Sprache

Englisch