|Assistent:||Philipp Paukner (Übung & Sprechstunde)|
|Zielgruppe:||Wahlfach, Ergänzungsvorlesung (Master)|
|Umfang:||2/1/0 (SWS Vorlesung/Übung/Praktikum)|
|Zeit & Ort:|| Vorlesung: Di. 13.15-14.45,|
Z995 Übung: Do. 15.00 - 16.30,
Z995 Sprechstunde: nach Vereinbarung
|Beginn:||erste Vorlesung am 10.04.2018|
Please note: This Lecture is part of the Modul "Data Analysis for Quality of Experience Assessments"
please register also to the lecture Quality of Experience
The course Data Analysis for Computer Engineering focuses on the acquiring practical skills for 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.
The course is built on a composition of various educational units, such as lecture presentations, reading assignments, flipped class room discussions, small projects and home works and at least on larger team project to perform a complete data analysis task.
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.
By the end of the course participants should have acquired the skills to plan and execute data collection and analysis campaigns in technical application scenarios.
We expect participating students to bring basic knowledge and experience in
- Programming (Matlab and/or Python)
- Elementary Signal Processing
- Elementary Statistics
- (Numerical) Linear Algebra
- Willingness and attitude to work
For collecting the credits the student are expected to
- participate in term projects and give a final presentation (50% of the final grade)
- hand in two assignments/homework (20% of the final grade)
- participate in an oral examination (30% of the final grade)