Time Tuesday, 8.15h-11.30h
Room S105
Credits 4 SWS / 6 ECTS
Exam written, 60min


  • First lesson in SS 18: 20.03.2018

Artificial Intelligence

Maybe you know Artificial Intelligence (AI) from Sci-Fi movies? With intelligent machines that are far superior to humans?

If you expect something like this, you might be on the wrong path. First because AI is not only fictitious and future stuff. It is already pervasive in our everyday life. AI is at the heart of weather forecasts, digital assistents (Amazon Echo, Siri, Google Now), recommendation systems, web-search, fraud detection, face recognition, medical- and technical diagnosis, computer games and much more. Even though we have lots of intelligent machines and applications and AI is supposed to bring major shifts in society, you should not be afraid: AI will not reach the full breadth of human intelligence soon. Intuition, emotions, common sense and social skills are only a few factors of intelligence, which make humans superior.

The message is: AI is something practical. It builds rational agents and it provides a toolbox, which enables new and improves existing applications. This lecture provides an insight in this AI toolbox. The elements of this toolbox are algorithms and datastructures. The mathematical and technical features of these algorithms and their application fields are in the focus of this lecture. On an abstract field AI, as covered in this lecture, can be partitioned into the 4 areas depicted in the figure below. For each area the algorithms and concepts, studied in this lecture, are listed.

Structure, Contents, Documents

Lecture Contents Additional Material
1. Introduction What is AI? Applications; Categorization; Definitions;
2. Search and Planning Heuristic and Non-Heuristic Search and Planning; Planning in Multiplayer Games; Monte Carlo Tree Search; Genetic Algorithm Python Implementation of Search Algorithms
3. Probabilistic Inference Probability Theory, Probabilistic Inference, Bayes Nets
5. Machine Learning Intro Definition, Application, Categorization, Evaluation Intro Supervised Learning - Regression, Intro Supervised Learning - Classification
6. Decision Trees / Ensembles Characteristics, Entropie, Information Gain, Learning, Boosting and Bagging, Random Forests Decision Tree Learning, Ensemble Learning
7. Neural Networks 1 General concepts, SLPs and MLPs for classification and regression, Stochastic Gradient Descent, Backpropagation Learning, SLP Implementation, MLP Implementation,
8. Deep Neural Networks Deep Neural Networks in general, Convolutional Neural Networks, Layer-Types, Architectures
9. Unsupervised Learning Preprocessing, Similarity Measures, k-means, Hierarchical Clustering, DBSCAN
3. Knowledge Representation and Inference Propositional Logic; First Order Logic; Inference Algorithms

Exercises and further materials


  • Artificial Intelligence: A Modern Approach (2nd Edition) (30 December 2002) by Stuart Russell, Peter Norvig
  • Grundkurs K√ľnstliche Intelligenz (31 January 2008) by Wolfgang Ertel
  • Introduction to Machine Learning (Adaptive Computation and Machine Learning) (01 October 2004) by Ethem Alpaydin
  • Programming collective intelligence : building smart web 2.0 applications (23 August 2007) by Toby Segaran