Universität Wien
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052113 VU Software Tools for Computational and Data Science (2025S)

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

Diese Lehrveranstaltung ist äquivalent zur VU "Software Tools and Libraries for Scientific Computing"
Moodle
Mo 03.03. 18:30-20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 05.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 10.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 17.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 19.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 24.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 26.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 31.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 02.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 07.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 09.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 28.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 30.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 05.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 07.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 12.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 14.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 19.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 21.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 26.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 28.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 02.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 04.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 11.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 16.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 18.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 23.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 25.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 30.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

We discuss software tools for computational and data science in the fields of linear algebra, gradient-based optimization, ordinary differential equations, sparse linear solvers, and neural networks, with a focus on learning-based methods, including their foundational numerical algorithms and GPU-accelerated computation using CUDA.

Students gain hands-on experience with computational software such as BLAS, PyTorch, LAPACK, MPFR, PETSc, and CUDA, and they learn to implement neural network models, automatic differentiation, and gradient-based training pipelines. Computational results are evaluated to ensure optimal performance. Hands-on experience is reinforced through in-depth discussions.

The students are expected to have good general programming skills, basic familiarity with programming in C and Python (other languages upon request), and experience in using GNU/Linux and Bash. The course builds upon the contents of the modules "Introduction to Numerical Computing" (NUM) and "Combinatorial and Numerical Algorithms" (CNA).

Assessment and permitted materials

The grading will be based on two written exams (closed book) and projects.

Minimum requirements and assessment criteria

Presence is mandatory during the entire course. Each part (projects and exams, respectively) needs a score of at least 50%; grading: <50%=5, 50% up to 62.5%=4, up to 75%=3, up to 87,5%=2, 87,5% or better=1.

Examination topics

All topics of the lectures will be relevant for the exams.

Reading list

The lectures are accompanied by slides which point to additional relevant literature (supplied in the course). Textbooks etc. are not required.

Association in the course directory

Module: STL

Last modified: Th 30.01.2025 05:06