Practical Concepts of Machine Learning
Dozent: Klaus Diepold
Umfang: 2/1/0 (SWS Vorlesung/Übung/Praktikum)
Turnus: Sommersemester
Zeit & Ort:
Beginn: erste Vorlesung


Please note: this lecture is part of a module and can be registered only in combination with Deep Learning for Multimedia!

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.