Universität Wien

053613 VU Introduction to Machine Learning (2022W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Monday 03.10. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Friday 07.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 10.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Friday 14.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 17.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 3, Währinger Straße 29 1.UG
Friday 21.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 24.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Friday 28.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 31.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 2, Währinger Straße 29 1.UG
Friday 04.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 07.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Friday 11.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 14.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 12, Währinger Straße 29 2.OG
Friday 18.11. 13:15 - 14:45 Digital
Monday 21.11. 15:00 - 16:30 Digital
Friday 25.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 28.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 4, Währinger Straße 29 1.UG
Friday 02.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 05.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Friday 09.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 12.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
Friday 16.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 09.01. 15:00 - 17:15 Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 10, Währinger Straße 29 2.OG
Friday 13.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 16.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Friday 20.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 23.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
Friday 27.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Monday 30.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG

Information

Aims, contents and method of the course

Goals:
Upon successful participation in the course, students will understand the fundamentals of machine learning and how to apply basic machine learning approaches/ideas in theory and practice.

Lecture Contents:
* What is Machine Learning?
* Basic Machine Learning pipelines
* Linear models for regression
* Linear models for classification
* Model validation and model selection
* Kernels
* Neural networks
* Dimensionality reduction
* Probabilistic modeling
* Generative modeling
* Deep generative models

Method:
Lecture + pen & paper exercises + programming exercises

Assessment and permitted materials

* Written exam: at the end of the semester; you will be allowed to bring 2 handwritten A4 sheets (4 pages) of notes

* Programming assignments: coding in Python; you will have to submit your executable source code & a written report on your implementation and results; some of the programming assignments include a peer-review of your colleagues' source code & report

* Pen & paper exercises: you will solve the pen & paper exercises at home; to be awarded credits for your solutions you have to present your solutions in the pen & paper exercises sessions (you will be randomly selected); pen & paper exercises will take place about every 2nd week

Minimum requirements and assessment criteria

30% Written exam
40% Programming exercises
30% Pen & paper exercises

P = Average weighted percentage on the written exam, the programming exercises, and the pen & paper exercises

90% <= P <= % Sehr Gut (1)
77% <= P < 90% Gut (2)
62% <= P < 77% Befriedigend (3)
50% <= P < 62% Genügend (4)
0% <= P < 50% Nicht Genügend (5)

To successfully complete the course you need to achieve at least 50% of the points of the written exam, 50% of the points on the pen & paper exercises, and 50% of the points on the programming assignments.

Attendance of the lecture parts of the course is voluntary but highly recommended. Attendance of the pen & paper exercise and the written exam is compulsory to pass the course.

Examination topics

The presented topics in the lecture (according to slides + exercises). Referenced Literature (as indicated in detail on the lecture slides).

Reading list

* Christopher Bishop, 2006, "Pattern Recognition and Machine Learning", Springer; available online: https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/

* Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", Springer; available online: https://web.stanford.edu/~hastie/ElemStatLearn/

* Tom Mitchell, 1997, "Machine Learning", McGraw Hill

* Shai Shalev-Shwartz and Shai Ben-David, 2014, "Understanding Machine Learning: From Theory to Algorithms", Cambridge University Press; available online: https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/

Association in the course directory

Last modified: Th 11.05.2023 11:27