Teaching

Dear Students,

we offer various courses and theses for both bachelor and master students in the field of data analytics. The following points out the basic ideas behind our teaching und supervision in order for you to better understand what to expect.

Objective: The superficial objective is to teach the principles of data analytics, our area of expertise. We feel this is the minimal objective which equips the students with tools to analyse data in various domains. Since these tools are inevitable to actually practice data analysis, this goal is particularly important to us. However, we also aspire to help learn why research is important and what research questions are worth following. This not only prepares the students for seminar, bachelor’s, and master’s theses but, more importantly, allows to contribute to research.

Intensity: The courses will most likely be relatively intense but, hopefully, rewarding for the majority. That is because the minimal objective of equipping students with data analytics tools requires to cover topics in basic math but also in programming.

Recommendation: If you take one of the courses, almost certainly, there will be programming exercises involved. Try to keep up solving and understanding them. The exercises are made to strengthen the understanding of the concepts taught in the lectures and are very closely following typical data analytics pipelines.

 

Bachelor Courses

Business Analytics: Technologien, Methoden und Konzepte

Business analytics as a discipline makes use of a variety of methodological and technological approaches for the analytical evaluation of company-relevant data from different source systems in order to gain insights into past, present and future business activities. Of interest are, for example, aggregated or filtered insights about the company’s performance or the uncovering of previously unknown correlations, trends and patterns in order to generate new knowledge and improve the company’s decisions. For this purpose, the approach makes use of different methods of diverse origin, such as from the fields of statistics, data mining and artificial intelligence.

The practice-oriented course introduces the basics of the topic and provides an overview of relevant concepts, methods and technologies. Here, the focus is particularly on the subarea of predictive analytics and the approaches of (supervised) machine learning for the creation of predictive models. Using a typical pipeline for data business analytics projects, the basic steps and principles of predictive modeling are illustrated and supported with example approaches (e.g., model training using deep neural networks). The course consists of a lecture to convey conceptual content and an accompanying computer-based exercise in which selected aspects are deepened and implemented using the Python programming language.

This course is taught together with Prof. Zschech (Intelligent Information Systems). For details about the course, see http://www.studon.fau.de .

 

Master Courses

Development of Deep Vision Systems

Computer vision systems try to mimic human capabilities of visual perception to support time-consuming and labor-intensive tasks like the recognition, localization, and tracking of critical objects. Nowadays, such systems increasingly rely on methods and tools from the field of machine learning to automatically extract useful information from images that can be utilized for decision support and business automation purposes.

This course provides the necessary fundamentals for the development of modern vision systems based on machine learning. The particular focus is on deep neural networks and their capabilities of automated feature learning. More specifically, we consider different types of network architectures, look at the steps of image labelling and data preparation, discuss crucial hyperparameters and evaluation criteria, and review other related aspects, such as 3D vision, hybrid intelligence, and explainable artificial intelligence.

The course has a strong practical focus. At the beginning of the semester, all fundamentals are provided in lecture sessions and hands-on exercises. Afterwards, students are encouraged to work (in groups) on real projects to apply the methods and concepts learned during the teaching sessions. The results are presented and discussed at the end of the semester.

This course is taught together with Prof. Zschech (Intelligent Information Systems). For details about the course, see http://www.studon.fau.de .

Natural Language Processing for Business Analytics

Over the last few years, natural language processing (NLP) has been one of the most revolutionary fields of artificial intelligence (AI). NLP gives machines the ability to extract meaning from human languages and make decisions based on this data. In other words, NLP helps computers communicate with humans in their own language.

This course provides the necessary fundamentals for the development of modern NLP systems based on machine learning. We cover a wide range of feature extraction and modeling techniques including recent innovations in the field of deep neural networks and their capabilities of automated feature learning. Additionally, we also look at further aspects such as ethical issues and the use of explainable artificial intelligence methods to gain insights about the functioning of learned models.

The course has a strong practical focus. At the beginning of the semester, all fundamentals are provided and students with less knowledge in programming have the opportunity to catch up in a bootcamp introductory session before learning the fundamentals in hands-on exercises. Afterwards, students are encouraged to work (in groups) on real projects to apply the methods and concepts learned during the teaching sessions. The results are presented and discussed at the end of the semester.

This course is taught together with Prof. Zschech (Intelligent Information Systems). For details about the course, see http://www.studon.fau.de .

Business Analytics: Case Studies

Business Analytics (BA) is a systematic approach that applies qualitative, quantitative, and statistical computational tools and methods to analyze data, gain insights, inform, and support decision-making. In this respect, methods from the field of machine learning (ML) have gained particular attention as they give computers the ability to perform tasks without being explicitly programmed to do so. Advances in ML enable the development of intelligent systems with human-like cognitive capacity that penetrate our business and personal life in every conceivable way. This is demonstrated by many diverse examples, such as fraud detection, predictive maintenance, credit scoring, next-best offer analysis, speech and image recognition, or natural language processing.

This course offers students, who already have a fundamental understanding of BA and ML, the opportunity to deepen their knowledge by developing data-driven processing pipelines and applying modern learning algorithms to solve real-world problems from research and practice. Students can either bring their own interesting BA/ML cases or are provided with exciting challenges from a predefined selection. Depending on the availability of open topics, there is also the chance to work on current cases from our collaboration partners.

The course has a strong practical focus and requires a high degree of self-initiative and dedication by the participants. At the beginning of the semester, some conceptual basics are repeated as a refresher. However, the in-depth investigation of relevant methods, procedures and principles required by the circumstances of the individual cases is done independently by the students in self-study. Students are encouraged to work (in groups) on the chosen projects to solve upcoming challenges in cooperation. To monitor the learning progress during the course, open consultation meetings are offered on a continuous basis, in which the applied approaches and procedures can be reflected in a participatory manner. The final results are presented and discussed at the end of the semester.

This course is taught together with Prof. Zschech (Intelligent Information Systems). For details about the course, see http://www.studon.fau.de .