Universität Wien

053613 VU Introduction to Machine Learning (2021W)

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

  • Friday 01.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 04.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 08.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 11.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 15.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 18.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 22.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 25.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 29.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 05.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 08.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 12.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 15.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 19.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 22.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 26.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 29.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 03.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 06.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 10.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 13.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 17.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 07.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 10.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 14.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 17.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 21.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 24.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 28.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 31.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG

Information

Aims, contents and method of the course

Due to the ongoing pandemic, this course will be taught in a hybrid format. Details will be discussed in the first lecture. Any online sessions will take place in the regular lecture times, will be recorded, and will be made available on Moodle.

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Update December 1st: Until further notice all lectures and exercise sessions will be held online. Links for joining are available through Moodle.
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Important: We will have the first lecture online (BBB) on Friday October 1st at 13:15.

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
Programming assignments
Pen & paper exercises

Minimum requirements and assessment criteria

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

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

87.5% <= P <= % Sehr Gut (1)
75% <= P < 87.5% Gut (2)
62% <= P < 75% Befriedigend (3)
50% <= P < 62% Genügend (4)
0% <= P < 50% Nicht Genügend (5)

To successfully complete the course you need to achieve at least 30% 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 .

Examination topics

The presented topics in the lecture (according to slides + exercises). Referenced Literature (as indicated in detail on 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: We 01.12.2021 14:48