Universität Wien

250141 VO Applied machine learning (2023S)

5.00 ECTS (3.00 SWS), SPL 25 - Mathematik
Moodle

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Details

Language: German

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

Vorbesprechung 1.3.2023 HS13 16:45

  • Wednesday 01.03. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 06.03. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 08.03. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 15.03. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 20.03. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 22.03. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 27.03. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 29.03. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 17.04. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 19.04. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 24.04. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 26.04. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 03.05. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 08.05. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 10.05. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 15.05. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 17.05. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 22.05. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 24.05. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 31.05. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 05.06. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 07.06. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 12.06. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 14.06. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 19.06. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 21.06. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 26.06. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 28.06. 16:45 - 18:15 Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

Machine learning plays an essential role almost everywhere in technology and research these days. The creation of data-driven models, which can be used efficiently as proxy and/or prediction models for new data, requires "know-how" in the mathematical basics as well as experience in dealing with the appropriate software packages.
The VU aims to "reconcile" theoretical and application-relevant aspects. In addition to the particularly practically relevant basics such as data analysis, model selection, validation, over- and underfitting, "feature selection", we will discuss "classical methods" such as non-linear model reduction (PCA, MDS, kernel methods, etc.), classification (logistic regression, random forests , SVMs, etc), regression (kernel rigde regression, lasso, etc), clustering, ensemble learning, as well as the basics of the current Deep Neural Networks (DNN), especially new advanced methodologies like autoencoders/-decoders, convolutional Neural Networks and " Physics-Informed Neural Networks (PINN)".
Using practical exercises, the handling of this is taught "hands-on" via Python, scikit-learn and keras/tensor flow, where we use the no-setup environment Kaggle with free GPU access. The VU will also deal with current research topics and applications in physics and materials research.

Structure:
The VO "Applied Machine Learning" combines (i) a "theory part" where mathematical and numerical basics of machine learning are presented, (ii) practical exercises that accompany the first part, and (iii) a "group work" where a small application problem in groups of about 2-6 people is considered and presented.

!!! There are separate accompanying exercise courses -> registration required: LV-Nr. 250144.

Assessment and permitted materials

The grade results from the team project and small "exams". Alternative to the presentation there is also the possibility to do an oral exam.

Minimum requirements and assessment criteria

The course imparts basic knowledge about "Machine Learning" using lectures, exercises and a small team project.

Examination topics

Presentation of team project in the end of semester

Reading list

Lecture notes.
Further literature:
Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O'Reilly Media, 2019.
Forsyth, David, Applied Machine Learning. Springer International Publishing, 2019.
Shalev-Shwartz, Shai, and Shai Ben-David, Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.

Association in the course directory

ZWM

Last modified: We 08.11.2023 08:07