053613 VU Introduction to Machine Learning (2024W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Fr 13.09.2024 09:00 bis Fr 20.09.2024 09:00
- Abmeldung bis Mo 14.10.2024 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Montag 07.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Freitag 11.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 14.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- N Freitag 18.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
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Montag
21.10.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 11, Währinger Straße 29 2.OG - Freitag 25.10. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 28.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 04.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Freitag 08.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
-
Montag
11.11.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 12, Währinger Straße 29 2.OG - Freitag 15.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 18.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Freitag 22.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 25.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Freitag 29.11. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 02.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Freitag 06.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
-
Montag
09.12.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 6, Währinger Straße 29 1.OG - Freitag 13.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 16.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Freitag 10.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
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Montag
13.01.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 5, Währinger Straße 29 1.UG - Freitag 17.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 20.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Freitag 24.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
- Montag 27.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Freitag 31.01. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
* 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)
(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)
Mindestanforderungen und Beurteilungsmaßstab
40% Written exam
30% Programming exercises
30% Pen & paper exercisesP = Average weighted percentage on the written exam, the programming exercises, and the pen & paper exercises90% <= 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.
30% Programming exercises
30% Pen & paper exercisesP = Average weighted percentage on the written exam, the programming exercises, and the pen & paper exercises90% <= 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.
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: Mo 14.10.2024 15:25
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 modelingMethod:
Lecture + pen & paper exercises + programming exercises with presentations