Universität Wien

053613 VU Introduction to Machine Learning (2023W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Montag 02.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 06.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 09.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 13.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 16.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 6, Währinger Straße 29 1.OG
Freitag 20.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 23.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 27.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 30.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 6, Währinger Straße 29 1.OG
Freitag 03.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 06.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 10.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 13.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 6, Währinger Straße 29 1.OG
Freitag 17.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 20.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 24.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 27.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 01.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 04.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Montag 11.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 15.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 08.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 12.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 15.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 19.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 22.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Freitag 26.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Montag 29.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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

Method:
Lecture + pen & paper exercises + programming exercises

Art der Leistungskontrolle und erlaubte Hilfsmittel

* 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

Mindestanforderungen und Beurteilungsmaßstab

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 <= 100% 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.

Prüfungsstoff

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

Literatur

* 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/

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 15.11.2023 13:27