Universität Wien

053613 VU Introduction to Machine Learning (2024W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle
Fr 18.10. 13:15-14:45 Hörsaal 2, Währinger Straße 29 2.OG

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 07.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 11.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 14.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 21.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 11, Währinger Straße 29 2.OG
  • Friday 25.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 28.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 04.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 08.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 11.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 12, Währinger Straße 29 2.OG
  • Friday 15.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 18.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 22.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 25.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 29.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 02.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 06.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 09.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 13.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 16.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 10.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 13.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 5, Währinger Straße 29 1.UG
  • Friday 17.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 20.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 24.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Monday 27.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 31.01. 13:15 - 14:45 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

Method:
Lecture + pen & paper exercises + programming exercises with presentations

Assessment and permitted materials

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

* Programming assignments:
(a) solving machine learning-related programming assignments in Python at home; you will have to submit your executable source code & a written report on your implementation and results; all tasks must be solved and submitted individually
(b) you will have to present and discuss your implementation and results with your peers in two in-person sessions

* Pen & paper exercises: you will solve 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)

Minimum requirements and assessment criteria

40% Written exam
30% 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 on each of the written exams, AND
* at least 50% of the points on the pen & paper exercises, AND
* at least 50% of the points on the programming assignments and their presentation.

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: Mo 14.10.2024 15:25