Lecture

Foundation of artificial intelligence (Grundlagen der künstlichen Intelligenz (GKI))

Staff

Language

  • German/English

Lecture

The lecture is to be held offline. Please refer to the Moodle course for further information.

  • Monday, 14:00-16:00
  • Room: LB 134

Exercise

  • Monday

    • 16:00-18:00, LC 137

  • In the first week there will be introductory programming exercises.

Topic

  • Introduction
  • Data Science (Collection, cleaning, preprocessing, visualization)
  • Search algorithm (DFS, BFS, PageRank, topological search (Kahn-Algorithm), Dijkstra, A*, UCS, …)
  • ML Methods (Methods, metrics, cross validation, …)
  • Genetic Algorithms (Crossover, mutation, tournaments, …)
  • Classification & Clustering (kNN, kMeans, DBSCAN, Decision Tree, Random Forest, hierarchical clustering, dimension reduction, …)
  • Regression (Linear, logistic, …)
  • Neural Networks (I/II) (Neurons, forward-propagation, architectures (CNN, GAN, AE, VAE), optimizer, loss-function, …)
  • Natural Language Processing (NLP) (classical and modern language processing, tf-idf, BERT, Transformer, …)
  • Explainability (XAI) (Explainability, Interpretability)
  • Ethics (Deep Fake, Fairness, Bias, Data Ethics, EU-AI Act)

Materials & Infos

Moodle-Course

To obtain the enrollment key, visit the first lecture.