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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.

2019S, VU, 3.0h, 4.5EC

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, 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: 5.3. 2019

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 - 14:0005.03.2019 - 25.06.2019EI 8 Pötzl HS Lectures
Di.09:00 - 13:0030.04.2019Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 1
Di.12:00 - 17:0030.04.2019Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 1
Do.10:00 - 12:0002.05.2019FAV Hörsaal 2 Presentations Exercise 1
Do.15:00 - 17:0002.05.2019FAV Hörsaal 2 Presentations Exercise 1
Fr.10:00 - 16:0003.05.2019FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 1
Fr.13:00 - 15:0017.05.2019EI 8 Pötzl HS Machine Learning
Mo.14:00 - 17:0027.05.2019FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 2
Di.10:00 - 16:0028.05.2019Seminarraum FAV 01 C (Seminarraum 188/2) Presentations Exercise 2
Mi.10:00 - 14:0029.05.2019Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 2
Mi.11:00 - 16:0029.05.2019FAV Hörsaal 2 Presentations Exercise 2
Machine Learning - Einzeltermine
TagDatumZeitOrtBeschreibung
Di.05.03.201912:00 - 14:00EI 8 Pötzl HS Lectures
Di.12.03.201912:00 - 14:00EI 8 Pötzl HS Lectures
Di.19.03.201912:00 - 14:00EI 8 Pötzl HS Lectures
Di.26.03.201912:00 - 14:00EI 8 Pötzl HS Lectures
Di.02.04.201912:00 - 14:00EI 8 Pötzl HS Lectures
Di.09.04.201912:00 - 14:00EI 8 Pötzl HS Lectures
Di.30.04.201909:00 - 13:00Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 1
Di.30.04.201912:00 - 14:00EI 8 Pötzl HS Lectures
Di.30.04.201912:00 - 17:00Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 1
Do.02.05.201910:00 - 12:00FAV Hörsaal 2 Presentations Exercise 1
Do.02.05.201915:00 - 17:00FAV Hörsaal 2 Presentations Exercise 1
Fr.03.05.201910:00 - 16:00FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 1
Di.07.05.201912:00 - 14:00EI 8 Pötzl HS Lectures
Di.14.05.201912:00 - 14:00EI 8 Pötzl HS Lectures
Fr.17.05.201913:00 - 15:00EI 8 Pötzl HS Machine Learning
Di.21.05.201912:00 - 14:00EI 8 Pötzl HS Lectures
Mo.27.05.201914:00 - 17:00FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 2
Di.28.05.201910:00 - 16:00Seminarraum FAV 01 C (Seminarraum 188/2) Presentations Exercise 2
Di.28.05.201912:00 - 14:00EI 8 Pötzl HS Lectures
Mi.29.05.201910:00 - 14:00Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 2

Leistungsnachweis

schriftliche Prüfung

Prüfungen

TagZeitDatumOrtPrüfungsmodusAnmeldefristAnmeldungPrüfung
Mi.12:00 - 14:0004.12.2019EI 9 Hlawka HS schriftlich18.10.2019 18:00 - 02.12.2019 23:59in TISSExam (2nd retake SS 2019)
Mi.15:00 - 17:0029.01.2020EI 7 Hörsaal schriftlich01.01.2020 00:00 - 26.01.2020 23:59in TISSExam (main date winter semester, last retake summer semester)
Fr.14:00 - 16:0020.03.2020HS 17 Friedrich Hartmann beurteilt31.01.2020 12:00 - 18.03.2020 00:00in TISSExam - 1st Retake
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
12.12.2018 12:00 30.04.2019 23:59 12.03.2019 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