E-LEARNING: Data Science II - 18-zo-2120-se

Lehrinhalte
The course covers the following topics:
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[*]Data Science Advanced Methods
[*]Data Management Big data frameworks
[*]Statistical Learning
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[*]Recommender Systems
[*]Deep Learning
[*]Unsupervised Learning
[*]Text data analysis
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[*]Final application project. Flexibility to choose from list of projects or come up with own project. Examples:
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[*]Sound classification
[*]Heart rate analysis
[*]Activity recognition with acceleration data
[*]Hyperspectral data
[*]Image classification
[*]Health survey
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Literature
Lecture notes and slides can be downloaded here:
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[*]http://www.spg.tu-darmstadt.de
[*]Moodle platform
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Further reading:
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[*]Wes McKinney: Python for Data Analysis, O’Reilly, 2017
[*]Christopher M. Bishop: Pattern Recognition and Machine Learning, 2011
[*]James, Witten, Hastie and Tibshirani, Introduction to Statistical Learning, Springer, 2017
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Voraussetzungen
Data Science I (Lecture)

Semester: WT 2021/22