Detailinformationen 
Quellcurriculum 
Masterstudium Artificial Intelligence 2023W 
Ziele 
^{(*)}Machine learning is concerned inferring models/relationships by learning from data. Machine learning methods are gaining importance in various fields, such as, process modeling, speech and image processing, and so forth. In recent years, bioinformatics has become one of the most prominent application areas of machine learning methods: The massive data amounts produced by recent and currently emerging highthroughput biotechnologies provide unprecedented potentials, but also pose yet unseen computational challenges in the analysis of biological data. Despite all potentials and successes of machine learning, one has to acknowledge that machine learning methods may produce poor or misleading results if they are applied inappropriately.
This course provides a look at the theoretical background of machine learning. The goal is to make students acquainted with the mathematical theories underlying machine learning methods in order to have a more profound understanding of the potentials and limits of machine learning.

Lehrinhalte 
^{(*)} Generalization error
 Biasvariance decomposition
 Error models
 Model comparisons
 Estimation theory
 Statistical learning theory
 Worstcase and average bounds on the generalization error
 Structural risk minimization
 Bayes framework
 Evidence framework for hyperparameter optimization
 Optimization techniques
 Theory of kernel methods

Beurteilungskriterien 
^{(*)}Exam (written or oral)

Lehrmethoden 
^{(*)}Slide presentations complemented by examples presented on the blackboard

Abhaltungssprache 
Englisch 
Literatur 
^{(*)}Electronic course material is made available for download

Lehrinhalte wechselnd? 
Nein 
Frühere Varianten 
Decken ebenfalls die Anforderungen des Curriculums ab (von  bis) INMAWVOTCML: VO Theoretical Concepts of Machine Learning (2007W2020S)
