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Event
Introduction to Neural Networks and Genetic Algorithms (SoSe 2023) [SS232540541]
Lecturers
Organisation
- KIT-Fakultät für Wirtschaftswissenschaften
Part of
- Brick Introduction to Neural Networks and Genetic Algorithms | Industrial Engineering and Management (B.Sc.)
- Brick Introduction to Neural Networks and Genetic Algorithms | Economics Engineering (B.Sc.)
- Brick Introduction to Neural Networks and Genetic Algorithms | Digital Economics (B.Sc.)
- Brick Introduction to Neural Networks and Genetic Algorithms | Information Systems (B.Sc.)
Literature
- Goldberg, David E. (2001)
Genetic Algorithms in Search, Optimization and Machine Learning.
Addison-Wesley, New York. - Bishop, Christopher M. (2006)
Pattern Recognition and Machine Learning.
Springer, New York. - Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016)
Deep Learning.
MIT Press. Cambridge.
Appointments
- 18.04.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 25.04.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 02.05.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 09.05.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 16.05.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 23.05.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 06.06.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 13.06.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 20.06.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 27.06.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 04.07.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 11.07.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 18.07.2023 15:45 - 17:15 - Room: 05.20 1C-01
- 25.07.2023 15:45 - 17:15 - Room: 05.20 1C-01
Note
The course consists of a short introduction and two parts:
- In the introduction, the biological mechanisms of neural and genetic methods are presented.
Furthermore, a common framework for the learning performance evaluation of these methods in applications is introduced. - In the field of genetic methods, simple genetic algorithms and their variants are introduced, analyzed, and applied.
- In the area of neural methods, the basic algorithms are presented (e.g., backpropagation) as well as their applications in data science.
Learning Objectives:
The student knows the essential algorithms, learning procedures, and methods for neural networks and genetic algorithms. They can apply these methods (e.g. in R) and evaluate their quality.