Intendierte Lernergebnisse
Students should recognize the issue of uncertainty inherent in many Artificial Intelligence applications, understand basic methods for dealing with this issue and learn to adopt and comprehend concrete algorithms that implement these methods. The focus in the first half of the semester will be on reasoning under uncertainty, whereas the second half will deal with supervised and unsupervised machine learning.
Lehrmethodik
Lecture mixed with practical home and in-class exercises. Slides will be in English. Teaching language will be German unless there are non-German-speaking participants, otherwise English.eLearningMoodle
Inhalt/e
Provides an introduction to selected methods for machine learning and approaches for dealing with uncertainty in Artificial Intelligence. TopicsUncertainty in AI SystemsBayesian Inference and Bayesian NetworksUnsupervised Machine LearningSupervised Machine Learning
Literatur
Adnan Darwiche. Modeling and Reasoning with Bayesian Networks. Cambridge University Press. 2009 Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Pearson. 2006 Stuart Russell, Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall. 2009 Judea Pearl. Probabilistic Reasoning in Intelligent Systems - Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc. 1988 Daphne Koller, Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press. 2009 David Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press. 2012 Tom Mitchell. Machine Learning. McGraw Hill. 1997Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning. Springer. 2021