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.

2021S, VU, 3.0h, 4.5EC
TUWEL

Properties

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

Learning outcomes

After successful completion of the course, students are able to...

- 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

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, Naive Bayes, Bayesian Networks, Basic Regression Techniques, Support Vector Machines, Random Forests, Perceptron, Neural Networks, Deep Learning, as well as ensemble methods. The course also gives a short introduction to Automated Machine Learning and Reinforcement Learning. 

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: 2.3. 2021, via Zoom: https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09

Teaching methods

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. 

Mode of examination

Immanent

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:

13 classes (including prepration): 34 h

3 classes for presentations/discussions (including preparation): 9

Assignments: 39.5 h

exam: 30 h

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

total: 112.5 h

 


 

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue12:00 - 14:0002.03.2021 - 29.06.2021 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09 (LIVE)Machine Learning Lecture
Machine Learning - Single appointments
DayDateTimeLocationDescription
Tue02.03.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue09.03.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue16.03.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue23.03.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue13.04.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue20.04.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue27.04.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue04.05.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue11.05.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue18.05.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue01.06.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue08.06.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue15.06.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue22.06.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture
Tue29.06.202112:00 - 14:00 https://tuwien.zoom.us/j/94709997068?pwd=T0pKdk9LYVJGc1FKQytIWU52RUtLUT09Machine Learning Lecture

Examination modalities

- 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%

- Written exam - most likely on-line via TUWEL. If the pandemic situation allows face-to-face exams at the scheduled time, we would switch to an in-class exam.

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Thu - 21.10.2021written06.10.2021 00:00 - 19.10.2021 23:59TISSExam (2nd retake SS2021)
Wed - 08.12.2021written20.10.2021 00:00 - 21.11.2021 23:59TISS*Cancelled due to TU CoVid regulations* Exam (3rd retake SS 2020, last retake for WS 2019)
Mon - 24.01.2022assessed13.12.2021 00:00 - 21.01.2022 23:59TISSExam (main date WS2020, 5th & final retake WS 2019, 4th & penultimate retake SS 2020)

Course registration

Begin End Deregistration end
16.12.2020 12:00 07.03.2021 23:59 02.03.2021 23:59

Registration modalities

Acceptance to the course will be by the lecturers. Priority is given to

1.) Students that have this course as a compulsory or elective course (i.e. most computer science studies)

2.) ERASMUS students that have Machine Learning in their learning agreement.

3.) PhD students from the Faculty of Informatics

4.) Students that are currently in a bachelor programme of any of the studies mentioned in 1.), and are finishing their studies in the current semester. You will need to contact the lectureres (Rudolf Mayer, Nysret Musliu) and state your expected graduation, and which master programme you will continue

5.) If there are still free places afterwards, they will be assigned to master and PhD students from other faculties, and finally to all other students from other faculties. You need to contact the lecturers (Rudolf Mayer, Nysret Musliu) and state why the course is important for your studies. Note that the registration can be confirmed only when for the registration period ends.

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