Ringvorlesung AI: - SoSe 25

Course Content

  • Symbolic AI [1]
    • Concept of Agents in AI
    • Problem solving by searching
    • Logic and Reasoning 
  • Statistical Learning
    • The students gain a principal basic understanding of the mathematical and epistemological basics of statistical learning theory and machine learning.
    • Basic Model of Statistical Learning following [2]
    • Different models such as random Forests, Neural SVMs, and Networks as black boxes
    • Generative Applications / Sampling basics of Text and Image Generation, n-gram Models, GANs
    • Training, testing, validation incl. Overfitting, Test-Train-Split, FP-FN-Tradeoff, ROC-Curves, F1-Score
  • Reinforcement Learning
    • The students gain a basic understanding of the concept of autonomous and adaptive agents in AI [3]. This includes an overview of established problem modeling frameworks and algorithmic approaches to implementing RL agents.
  • Ethical implications for society, principal problems of algorithmic decisions (Information privacy, individual freedom, checks-and balances of citizens, corporations, states etc.)
  • The students have the opportunity to test their knowledge with prototypes in the exercises


Upon completion of the module the student is able to

  • Understand the terminology used in modern artificial intelligence including symbolic artificial intelligence, expert-base systems, reasoning, (un- and supervised) statistical learning and reinforcement learning
  • understand the core concepts of artificial intelligence focusing on the practical level, including primarily testing, validation and interpretation
  • realize a realistic small application using existing frameworks 
  • validate the application’s AI model
  • explain and interpret a model’s predictions properly
  • are able to assess the ethical and societal dimensions of applications