184.702 Machine Learning
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2019S, VU, 3.0h, 4.5EC
TUWEL

Properties

  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise

Aim of course

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

Subject of course

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

Additional information

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

 

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue12:00 - 14:0005.03.2019 - 25.06.2019EI 8 Pötzl HS Lectures
Tue09:00 - 13:0030.04.2019Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 1
Tue12:00 - 17:0030.04.2019Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 1
Thu10:00 - 12:0002.05.2019FAV Hörsaal 2 Presentations Exercise 1
Thu15:00 - 17:0002.05.2019FAV Hörsaal 2 Presentations Exercise 1
Fri10:00 - 16:0003.05.2019FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 1
Fri13:00 - 15:0017.05.2019EI 8 Pötzl HS Machine Learning
Mon14:00 - 17:0027.05.2019FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 2
Tue10:00 - 16:0028.05.2019Seminarraum FAV 01 C (Seminarraum 188/2) Presentations Exercise 2
Wed10:00 - 14:0029.05.2019Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 2
Wed11:00 - 16:0029.05.2019FAV Hörsaal 2 Presentations Exercise 2
Mon11:00 - 12:0013.01.2020Seminarraum FAV 01 B (Seminarraum 187/2) Machine Learning - Exam Inspection
Machine Learning - Single appointments
DayDateTimeLocationDescription
Tue05.03.201912:00 - 14:00EI 8 Pötzl HS Lectures
Tue12.03.201912:00 - 14:00EI 8 Pötzl HS Lectures
Tue19.03.201912:00 - 14:00EI 8 Pötzl HS Lectures
Tue26.03.201912:00 - 14:00EI 8 Pötzl HS Lectures
Tue02.04.201912:00 - 14:00EI 8 Pötzl HS Lectures
Tue09.04.201912:00 - 14:00EI 8 Pötzl HS Lectures
Tue30.04.201909:00 - 13:00Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 1
Tue30.04.201912:00 - 14:00EI 8 Pötzl HS Lectures
Tue30.04.201912:00 - 17:00Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 1
Thu02.05.201910:00 - 12:00FAV Hörsaal 2 Presentations Exercise 1
Thu02.05.201915:00 - 17:00FAV Hörsaal 2 Presentations Exercise 1
Fri03.05.201910:00 - 16:00FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 1
Tue07.05.201912:00 - 14:00EI 8 Pötzl HS Lectures
Tue14.05.201912:00 - 14:00EI 8 Pötzl HS Lectures
Fri17.05.201913:00 - 15:00EI 8 Pötzl HS Machine Learning
Tue21.05.201912:00 - 14:00EI 8 Pötzl HS Lectures
Mon27.05.201914:00 - 17:00FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 2
Tue28.05.201910:00 - 16:00Seminarraum FAV 01 C (Seminarraum 188/2) Presentations Exercise 2
Tue28.05.201912:00 - 14:00EI 8 Pötzl HS Lectures
Wed29.05.201910:00 - 14:00Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 2

Examination modalities

written exam

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Fri15:00 - 17:0016.10.2020 assessed25.09.2020 00:00 - 14.10.2020 23:59TISSExam (date not yet confirmed, depends on the COVID situation!)
Wed12:00 - 14:0009.12.2020 written23.10.2020 18:00 - 07.12.2020 23:59TISSExam (date not yet confirmed, depends on the COVID situation!)
Fri - 19.03.2021assessed29.01.2021 12:00 - 17.03.2021 00:00TISSExam (date not yet confirmed, depends on the COVID situation!)
Wed - 19.05.2021assessed22.04.2021 00:00 - 17.05.2021 23:59TISSExam (date not yet confirmed, depends on the COVID situation!)
Thu - 24.06.2021written31.05.2021 00:00 - 23.06.2021 23:59TISSExam FH1 (apply at the main exam, you will be assigned manually to a lecture room)
Thu - 24.06.2021written31.05.2021 00:00 - 23.06.2021 23:59TISSExam FH5 (apply at the main exam, you will be assigned manually to a lecture room)
Thu - 24.06.2021written31.05.2021 00:00 - 23.06.2021 23:59TISSExam FH8 Nöbauer HS (apply at the main exam, you will be assigned manually to a lecture room)
Thu08:00 - 10:0024.06.2021Prechtlsaal großer Teil - Achtung! Werkraum, kein Hörsaal! written19.05.2021 00:00 - 21.06.2021 12:00TISSExam - apply here (main date summer semester, last retake winter semester)
Thu08:00 - 10:0024.06.2021 written19.05.2021 00:00 - 21.06.2021 12:00TISSExam - apply here (main date summer semester, last retake winter semester)
Thu08:00 - 10:0024.06.2021 written19.05.2021 00:00 - 21.06.2021 12:00TISSExam - apply here (main date summer semester, last retake winter semester)
Thu08:00 - 10:0024.06.2021 written19.05.2021 00:00 - 21.06.2021 12:00TISSExam - apply here (main date summer semester, last retake winter semester)
Thu08:00 - 10:0024.06.2021 written31.05.2021 00:00 - 23.06.2021 23:59TISSExam FH6 (apply at the main exam, you will be assigned manually to a lecture room)

Course registration

Begin End Deregistration end
12.12.2018 12:00 30.04.2019 23:59 12.03.2019 23:59

Curricula

Literature

No lecture notes are available.

Previous knowledge

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.

Accompanying courses

Continuative courses

Language

English