Foundations of Machine Learning

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INFB Foundations of Machine Learning		Course	INF
Lecturers :	Prof. Dr. Georg Merz Prof. Dr. Emanuel Kitzelmann eMail	Term	4
Course Classification :	Bachelor Informatik, Profil-Katalog B-INF-Profil	CH	4
Language :	Englisch	Type	VÜ
Type of examination :	PL	Credits	5
Method of evaluation :	term paper with oral examination
Requirements :
Cross References :
Previous knowledges :	Mathematics II Programming II
Aids and special features :	Mode of asessment Coursework with oral examination Graded: yes Continuous Evaluation for assignments.
Teaching aims :	The students know and understand basic concepts and different types of machine learning and selected models. They are able to evaluate and analyze data from different application areas and to select, train and evaluate suitable models. They can describe selected models in more detail. They know selected ML libraries and are able to apply them to practical problems.
Contents :	• Motivation, introduction and basic concepts • Clustering and dimensionality reduction • Linear regression, gradient descent • Decision trees • Classification and regression • Over-/Underfitting • Data analysis and data engineering • Metrics for model evaluation • Cross validation • Ensemble learning • Neural networks • Other selected models of supervised and unsupervised learning • Practice with Python and Scikit-Learn
Literature :	Skript/Folien zur Lehrveranstaltung in Moodle Andriy Burkov: The Hundred-Page Machine Learning Book, 2019 Christopher M. Bishop: Pattern Recognition and Machine Learning, Springer, 2006 Stuart Russel, Peter Norvig: Artificial Intelligence: A Modern Approach, Pearson, 4. Edition, 2020

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