Applied Machine Learning - Practical Concepts of Machine Learning
Course 0000004277 in SS 2019
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 3 SWS |
| Organisational Unit | Chair of Data Processing (Prof. Diepold) |
| Lecturers |
Klaus Diepold Matthias Kissel Assistants: Philipp Paukner |
| Dates |
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | 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 course (e. g. Moodle) TUMonline entry |