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