GMU:Learning Machines: Difference between revisions

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(The course gives an insight into the functionality of machine learning systems and is intended to convey the theoretical and practical handling of this technology.)
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The course gives an insight into the functionality of machine learning systems and is intended to convey the theoretical and practical handling of this technology. In addition to the ability to artistically and critically reflect, the focus is on communication competence with the faculties of computer science.
The course gives an insight into the functionality of machine learning systems and is intended to convey the theoretical and practical handling of this technology. In addition to the ability to artistically and critically reflect, the focus is on communication competence with the faculties of computer science.


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* Visualization
* Visualization


Application:
*Installation of Tensorflow, GPU enable (CUDA etc.), introduction to Tensorboard
*Classification and Object Detection
*Practical applications for artists, introduction to scripts (style transfer, Pix2Pix)


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Revision as of 11:30, 4 October 2019

21.10. bis 25.10.2019, 25.11. bis 29.11.2019

Der Kurs gibt einen Einblick in die Funktionsweise von Machine Learning Systemen und soll den theoretischen und praktischen Umgang mit dieser Technologie vermitteln. Neben der Befähigung zur künstlerischen und kritischen Reflexion, steht die Kommunikationskompetenz mit den Fachbereichen der Informatik im Vordergrund.

Theoretische Grundlagen:

  • Theoretische Einführung in die Geschichte der AI (Kybernetik bis Machine Leraning)
  • Begriffsdefinitionen (Was ist „Künstliche Intelligenz“ etc.)
  • Definitionen der Verschiedenen Arten von Machine Learning
  • Kurze Erläuterung der mathematischen Grundlagen
  • Exkurs über Datensätze und Training
  • Reflektion über Sprachauffassung

Praktische Grundlagen Block I – Big Data 21.10. bis 25.10.2019 :

  • Einführung in die Benutzung von Jupyter Notebooks
  • Research nach Datensätzen
  • Programmierung intelligenter Systeme mit Scikit-Learn
  • Visualisierung

Praktische Grundlagen Block II – Natural Language Processing (NLP) 25.11. bis 29.11.2019:

  • Einführung in NLP
  • Nutzung von NLTK
  • Grundlagen Word2vec
  • Visualisierung



The course gives an insight into the functionality of machine learning systems and is intended to convey the theoretical and practical handling of this technology. In addition to the ability to artistically and critically reflect, the focus is on communication competence with the faculties of computer science.

Theoretical basics:

  • Theoretical introduction to the history of AI (cybernetics to machine learning)
  • Definitions of terms (What is "artificial intelligence" etc.)
  • Definitions of the Different Types of Machine Learning
  • Short explanation of the mathematical basics
  • Excursus on data sets and training
  • Reflection on language perception

Practical Basics Block I - Big Data 21.10. to 25.10.2019 :

  • Introduction to the use of Jupyter notebooks
  • Research for data sets
  • Programming of intelligent systems with Scikit-Learn
  • Visualization

Practical Basics Block II - Natural Language Processing (NLP) 25.11. to 29.11.2019:

  • Introduction to NLP
  • Use of NLTK
  • Basics Word2vec
  • Visualization


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Dr.phil.Alexander König http://www.media-art-theory.com