inf536 - Computational Intelligence II

inf536 - Computational Intelligence II

Department of Computing Science 6 KP
Module components Semester courses Summer semester 2024 Examination
Lecture
  • Unlimited access 2.01.536 - Convolutional Neural Networks Show lecturers
    • Prof. Dr. Oliver Kramer

    The course times are not decided yet.
    The lecture "Convolutional Neural Networks" gives an introduction to neural networks via MLP, Backpropagation, Convolutional Layers and ResNet. In the exercises the concepts will be programmed in Python and Keras.
Exercises
  • Unlimited access 2.01.536 - Convolutional Neural Networks Show lecturers
    • Prof. Dr. Oliver Kramer

    The course times are not decided yet.
    The lecture "Convolutional Neural Networks" gives an introduction to neural networks via MLP, Backpropagation, Convolutional Layers and ResNet. In the exercises the concepts will be programmed in Python and Keras.
Hinweise zum Modul
Prerequisites

useful previous knowledge: Linear Algebra, Stochastics
Prüfungszeiten

lecture-free period at the end of the semester
Module examination

written exam, e-exam
Skills to be acquired in this module

In the lecture "Convolutional Neural Networks" you will learn the basics of Convolutional Neural Networks, from methodological understanding to implementation.
Professional competence
The Students:

  • will learn Deep Learning expertise, which are essential qualifications as AI experts and Data Scientists.


Methodological competence

The Students:

  • learn the methods mentioned as well as the implementation in Python, NymPy and Keras.

Social competence
The Students:

  • are encouraged to discuss the taught content in groups and work together to implement the programming tasks in the exercises


Self-competence
The Students:

  • are guided to conduct independent research on advanced methods as the teaching field changes dynamically

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