Universität Wien

270087 VO Machine learning for molecules and materials (2024W)

4.00 ECTS (2.00 SWS), SPL 27 - Chemie
Moodle

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Details

max. 24 participants
Language: German

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

First meeting 1.10. at 13-14 in PC Pool (Währinger Str. 17, 2nd floor)
Lecture every Tuesday (8.10. - onwards) at 13-14:30 in Seminar room 1 (Währinger Straße 42),

Exercises every Monday (starting 7.10.) at 10-12 in PC Pool (Währinger Str. 17, 2nd floor)

  • Tuesday 08.10. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 15.10. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 22.10. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 29.10. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 05.11. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 12.11. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 19.11. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 26.11. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 03.12. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 10.12. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 17.12. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 07.01. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 14.01. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 21.01. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1
  • Tuesday 28.01. 13:00 - 14:30 Seminarraum 1 Analytische Chemie 2.OG Boltzmanngasse 1

Information

Aims, contents and method of the course

The lecture will be held in English.

The focus of this lecture is the derivation of various algorithms. The application of those will happen in the associated seminar.

This course provides a comprehensive introduction to machine learning, covering fundamental concepts and techniques used in the field. Students will explore supervised and unsupervised learning methods, including regression, classification, clustering, and dimensionality reduction. Key topics include overfitting, model evaluation, and the bias-variance tradeoff.

The course also delves into advanced methods such as neural networks and probabilistic approaches like Gaussian processes. Practical applications and hands-on exercises will enable students to implement and experiment with various machine learning algorithms using popular libraries. By the end of the course, students will be equipped with the knowledge and skills to tackle real-world machine learning problems and understand the principles behind the algorithms they use.

The hands-on exercises will be presented and discussed in 270095-1. Please make sure to attend this seminar as well.

Assessment and permitted materials

Performance will be assessed through a (most likely) oral exam at the end of the term.

Minimum requirements and assessment criteria

You will need basic knowledge in:
- Maths (e.g., matrix multiplication, computing derivatives)
- Programming (ideally Python) as can be acquired in, e.g., "Programming in C/Fortran/Python" or "Computational data processing"

This lecture will be graded based on a single oral exam. The grade will reflect the how good of a grasp one has on the concepts discussed in the lecture.

Examination topics

Content of the lecture.

Reading list

- lecture script

alternatively:
- C. Bishop, Pattern recognition and machine Learning
- https://www.deeplearningbook.org/
- research articles

Association in the course directory

CH-MAT-01, WD3, Design

Last modified: Tu 11.03.2025 12:06