Applied Machine Learning - Practical Concepts of Machine Learning
Lehrveranstaltung 0000004277 im SS 2019
Basisdaten
LV-Art | Vorlesung mit integrierten Übungen |
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Umfang | 3 SWS |
betreuende Organisation | Lehrstuhl für Datenverarbeitung (Prof. Diepold) |
Dozent(inn)en |
Klaus Diepold Matthias Kissel Mitwirkende: Philipp Paukner |
Termine |
weitere Informationen
Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).
ergänzende Hinweise | Practical Concepts of Machine Learning: The course Practical Concepts of Machine Learning focuses on the acquiring practical skills for applying concepts of machine learning in analyzing data, which come from a wide range of data sources. We will discuss and exercise methods for ▪ planning a data collection campaign, a test procedure or measurements and experiments ▪ exploring the collected data to search for structure and meaningful patterns hidden in the data ▪ building prediction models and classifiers to capture the essence of the phenomena comprised in data ▪ exploiting human cognition and integrating domain knowledge All these methods are presented along practical examples of data processing and analyzing, covering a wide range of applications, which are representative to the field of computer engineering. The style of the course is focusing on practical aspects built on top of theoretical foundations. The presented methods directly will lead to Data Mining and Big Data topics. We will implement numerical algorithms, visualize and process the data, evaluate and validate prediction models and discuss various implementation platforms (computer architectures) for efficient data analysis. |
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Links |
E-Learning-Kurs (z. B. Moodle) TUMonline-Eintrag |