scientific-python-course/CITATION.cff at main · Ceyron/scientific-python-course · GitHub

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cff-version: 1.2.0
message: "If you use these course materials in your teaching or research, please cite as below."
type: software
title: "An Introductory Course to Scientific Computing & Machine Learning in Python"
authors:
  - given-names: Felix
    family-names: Koehler
repository-code: "https://github.com/Ceyron/scientific-python-course"
license: MIT
keywords:
  - "Python"
  - "scientific computing"
  - "machine learning"
  - "NumPy"
  - "Matplotlib"
  - "SciPy"
  - "Scikit-Learn"
  - "TensorFlow"
  - "Keras"
  - "educational resources"
  - "course materials"
identifiers:
  - type: url
    value: "https://youtu.be/CIRGYcCasyI"
    description: "Course recording, part 1"
  - type: url
    value: "https://youtu.be/csEIJvP4z2s"
    description: "Course recording, part 2"
  - type: url
    value: "https://youtu.be/KQ-3KtgZMO4"
    description: "Course recording, part 3"
abstract: >-
  Slides, source code, and data for the first two days of an introductory
  three-day course on scientific computing and machine learning in Python,
  originally taught at TU Braunschweig. Covers NumPy, Matplotlib, SciPy,
  Scikit-Learn, and TensorFlow/Keras. Targeted at learners with some prior
  programming experience (e.g. in MATLAB) and a basic understanding of
  linear algebra. Designed as an interactive code-along course; full video
  recordings of the sessions are available on YouTube.