Course Catalogue

Advanced Software and Systems Engineering

Large-scale software-based systems are ubiquitous in today’s world and are central to the efficient automated handling of digitalized processes across domains. The goal of this course is to equip students with the fundamental knowledge and competence to be able to take architectural design decisions during the development of such systems while considering relevant trade-offs. This will provide students with a solid foundation to evaluate architectural proposals and design decisions regarding current and future software systems.

Students will substantiate this theoretical know-how during a semester-long implementation project, where they will develop a distributed software system in a team. The students will apply architectural patterns and state-of-the art software engineering practices to conceive, build, test, and deploy the system.

Prerequisites:
Object-oriented programming, software design principles and patterns, software engineering basics, distributed systems and computer networks basics, experience with the implementation of simple software systems.

Semester: Autumn semester
Credits: 6 ECTS

Cyber Security

Security is becoming increasingly important to guarantee reliable data storage, communication, and electronic transactions. This is an introductory course to cybersecurity. The main goal is to provide the students with means to reason about security and privacy. More precisely it aims to provide to the students: an overview of basic information and network security concepts and methods as well as a good knowledge of some commonly used network and cryptographic tools and protocols. The students will get a sound understanding of theory and implementation. We will describe attacks against existing schemes and how they can be avoided. The students will gain an appreciation of the difficulties involved in employing security protocols and tools to build secure systems.

Prerequisites:
We assume that the students are familiar with basic notions of TCP/IP and basic notions of programming.

Semester: Autumn semester
Credits: 6 ECTS
 

Data Science

In the last decades, the volume of processed and digitally stored data has reached huge dimensions. This volume is frequently not tractable by traditional methods of information processing.

This has led to a search for innovative methods capable of coping with large data volumes. These technologies complement the findings and methods developed by classical disciplines concerned with data analysis, first of all statistics.

Given the data, collected by sensing or by other data accumulation processes, the data science tasks consist in recognizing patterns interesting or important in some defined sense. More concretely, these tasks can adopt the form of the following variants (but not limited to them):

• Recognising one of the predefined classes of patterns
• Finding a quantitative relationship between some data
• Finding characteristics of data that are substantially more compact than the original data (data compression)

The processing of these tasks may be static or dynamic. The static variant is characterized by a fixed data set in which a pattern is to be recognized.  With dynamic processing, the recognition takes place on a stream of data provided continuously in time. 

This course covers the following topics: Data Science and its tasks, explorative data analysis, data sampling and distribution, statistical experiments and significance testing, application specific sapping and measuring the fit of data, continuous mapping, classification, dynamic systems, spatial systems, representation with reduced dimensionality, data processing, learning and generalisation, numerical algorithms, black box models and specific problems of some application cases.

Prerequisites:
Bachelor level knowledge of statistics, bachelor level knowledge of linear algebra, fundamentals of databases, python programming.

Semester: Autumn semester
Credits: 6 ECTS

Human-Computer Interaction

In this lecture we discuss core topics in human-computer interaction. Human-computer interaction (HCI) is a discipline concerned with the design, evaluation, and implementation of interactive computing systems. In this lecture we learn about human cognition and ergonomics, interaction paradigms and devices, the design process, design on different levels, and evaluation methods. We explore the human information processing from a computer science perspective as well as ergonomic and physiological principles, such as motor function or the sense of sight. The focus is on the technical realisation of user interfaces with high usability and great user experience. This includes input and output devices, interaction styles and paradigms of human-computer interaction. With usability engineering, a systematic, user-centred design process is presented that includes requirements analysis, task analysis, scenarios, and prototyping. Design aspects are dealt with at the levels of activity, information, and interaction design.

Semester: Autumn semester
Credits: 6 ECTS

Machine Learning

Over the last years, Artificial Intelligence (AI) has seen a steep rise to the top with large tech companies such as Google, Facebook and Amazon investing heavily in research and development. From chatbots, face recognition algorithm to self‑driving cars, AI is quickly transforming technology, business and society. The driving factors behind this momentum are recent advances in machine learning and in particular deep learning.

This lecture aims to introduce the concepts of Machine Learning (ML). The goal is to provide a broad overview in this field, understand the relationship between AI and ML, and to cover major areas of ML. After the successful completion of this lecture, students should have acquired a terminology in AI and ML and be able to understand its basic concepts. Further, ML coding experience in Python should have been acquired during self‐study and exercises by the implementation of approaches in this field.

Lecturer: Prof. Dr. Damian Borth
Semester: Spring semester
Credits: 6 ECTS

Natural Language Processing with Deep Learning

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, etc. In recent years, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models.

Topics:

• Word Vectors
• Backprop and Neural Networks
• Dependency Parsing
• PyTorch Tutorial Session 
• Recurrent Neural Networks and Language Models 
• Vanishing Gradients, Fancy RNNs, Seq2Seq 
• Machine Translation, Attention, Subword Models 
• Transformers
• Transformers and Pretraining
• Question Answering
• Natural Language Generation
• Reference in Language and Coreference Resolution
• Large Language models
• Integrating knowledge in language models
• Model Analysis and Explanation
• Future of NLP + Deep Learning

Lecturer: Prof. Dr. Siegfried Handschuh
Semester: Spring semester
Credits: 6 ECTS

Advanced Databases

Course discription to be published in summer 2022.

Lecturer: tbd
Semester: Autumn semester
Credits: 6 ECTS

Deep Learning

Course discription to be published in summer 2022.

Lecturer: Prof. Dr. Damian Borth
Semester: Autumn semester
Credits: 6 ECTS

Event-driven and Process-oriented Architectures

Aim of the course is to address critical challenges of modern event‑driven software architectures by integrating them with process‑oriented architectures. Furthermore, the course will enable students to apply event stream processing to derive business relevant events from IoT data that can be integrated into event‑driven applications (e.g., event‑driven microservices, business processes).
Event‑driven architecture (EDA) is a software architecture paradigm promoting the production, consumption of, and reaction to events.An EDA uses events to trigger and communicate between decoupled services and is common in modern applications built with microservices.While event‑driven architectures are well suited when elasticity, scalability, evolvability, and fault tolerance matter, it can be challenging to handle workflow or transactions in distributed systems.
The course discusses major trade‑offs and covers different ways how to use a process engine (i.e., Camunda and / or Zeebe) to achieve such a balance, combining event‑driven and process‑oriented architectures. Moreover, the course shows how a process engine can help when distributed transactions are needed.
Furthermore, the course will discuss how event stream processing is used to integrate events into software systems. The processing of events requires the application of stream processing technology (Kafka) to transform low‑level events (e.g., events collected by IoT devices such as our smart factory) to higher level domain events relevant for the system under development (e.g., using Siddhi and/or Kafka Streams / KSQL). This transformation from low‑level IoT events to high‑level domain events can also be supported by machine learning components for more complex data.

Prerequisites:

• Java and Spring Boot
• Fundamentals of software architectures
• Experience with usage of Continuous Integration / Continuous Deployment

Lecturer: Prof. Dr. Barbara Weber
Semester: Spring semester
Credits: 6 ECTS

Web-based Autonomous Systems

The Web pervades our everyday lives and provides a foundation for most software systems deployed on the Internet. Modern web‑based systems are inherently complex, heterogeneous, and increasingly dynamic, especially in the context of the Web of Things. In such systems, traditional engineering methods become impractical ‑‑ and more advanced methods are needed to engineer autonomous systems that can plan, learn, and adapt to achieve their design objectives more flexibly. This course offers a more in‑depth understanding of modern Web‑based systems and draws from the study of artificial intelligence to equip students
with the theoretical and practical knowledge for engineering Web‑based autonomous systems.
The first half of the course will cover fundamental topics for engineering Web‑based autonomous systems: among others, we will revisit the Web Architecture, we will discuss in more detail knowledge representation and Linked Data on the Web, we will look at various architectures for designing and programming autonomous software agents, and we will discuss the design of multiagent systems composed of agents that communicate with one another.
In the second half of the course, we will continue with more advanced topics such as coordination mechanisms, learning, and self‑organization in multi‑agent systems. All topics will be supported by practical exercise sessions that will offer hands‑on experience.

Prerequisites:

• Master Base Modules (especially the course ʺAdvanced Software and Systems Engineeringʺ)
• Object‑oriented programming
• Software engineering basics
• Distributed systems and computer networks basics
• Experience with the implementation of simple software systems
  

Lecturers: Prof. Dr. Mayer Simon and Dr. Andrei Ciortea
Semester: Spring semester
Credits: 6 ECTS

Software Assessment: From Planning to Experimentation

The course description will be published in summer 2022.

Lecturers: Prof. Dr. Johannes Schöning and Prof. Dr. Barbara Weber
Semester: Autumn semester
Credits: 6 ECTS

Ubiquitous Computing

The course description will be published in summer 2022.

Lecturer: Prof. Dr. Mayer Simon
Semester: Autumn semester
Credits: 6 ECTS

New venture creation: from business idea to validation and launch

This start-up creation course is conceived as an experiential-based learning process, whereas, through project work in teams, student get to live a real-life entrepreneurial experience. The course will offer the needed tools and methodologies for starting up, and will guide the students through applying them first-hand in practice, increasing the likelihood of business success.

In teams, and guided by the lecturer, students will:

• identify a new business idea based on an innovative technology, in line with UN’s 17 Strategic Development Goals (methodology: problem-based idea generation).
• test and validate the idea and the business model in the real world with potential customers and business partners (methodologies used: hypothesis-driven entrepreneurship, the lean start-up etc.).
• write an effective business proposal to be presented to early-stage investors or incubator programs, and to apply for national start-up competitions.
• present the results achieved in the business proposal in front of a jury of experts in the form of an elevator pitch.

Local start-up practitioners such as business angels, venture capital investors, entrepreneurs, incubator and accelerator managers, and public agency representatives will participate in the course as guest speakers or jurors.

Lecturer: Prof. Dr. Silvia Stroe
Semester: Autumn semester
Credits: 4 ECTS

Business Model Design

Business models must continually evolve in today's marketplace to ensure long-term competitive advantage. The future of competitiveness will not be between products or processes, but between business models. At the Institute of Technology Management at the University of St Gallen, Oliver Gassmann and his team developed a powerful methodology – The Business Model Navigator – that helps managers to systematically innovate their business models. The basic idea is to draw on business model patterns to innovate and revolutionize a todays business.
Through extensive research with over 350 business models, the team found that 90% of their innovations were made up of previously existing concepts, ideas, or business models. Using this information, they developed the 55+ business model patterns which will enable students to learn how to innovate business models.
During the course the students will meet real challenges from companies, where the students have to develop new business models including MVPs to test those business models.

Prerequisites: Pre-reading of the book:
Gassmann, O., Frankenberger, K, & Csik, M. (2020). The Business Model Navigator: The strategies behind the most successful companies. Harlow, UK: Pearson Education Ltd.

Lecturer: Prof. Dr. Oliver Gassmann
Semester: Spring semester
Credits: 4 ECTS

Engineering the Companies Digital Core

The St.Galler House of Digital Business, as the foundation to build the companies digital core:

The digitalization of society is changing the way we consume, work, live, and communicate with each other. Digitization is generating a new start-up era, which focuses on digital users and their changing needs, which opens up access to whole new markets. Digitization is also forcing established companies to build a digital core and therefore adapt to the changing needs of the digital society. The transformation from the information society to the digital society represents one of the key challenges for companies. This leads to that existing business models are becoming partially ineffective, value chains are changing to networks and systems, high costs are being incurred for the management of digitization, and new organizational forms and task profiles are emerging.

Based on the orientation to digital users and their needs, organizational strategies, processes, and systems as well as management tasks, products and services must be designed accordingly to build the digital core. Using model-based design theory, helps to develop innovative business solutions, to support companies in their digital transformation. Therefore, the aim of the course “Engineering the Companies Digital Core” is to enable students to acquire skills to design the companies’ digital core and help them in this transformation process, by actively take into account the digitalization of the economy and society. 

Parallel to the lecture, the students apply the methods they have learned in a running case (self-study). In addition to their work on the case, the students examine and evaluate the solutions of their fellow students. This is intended to promote action and transfer skills. Practical presentations by decision-makers from industry, will provide interesting insights into the practical relevance of the course content.

Innovative IT-based learning tools are used to promote learning objectives and to increase interactivity during and after the lecture. The content, process, components, and assessment criteria of the decentralized examination will be discussed in detail during the lecture.

Lecturers: Prof. Dr. Jan Marco Leimeister and Dr. Roman Rietsche
Semester: Spring semester
Credits: 4 ECTS

Performance Management in Start-ups

In short: The course incorporates financial decisionmaking at every stage of the entrepreneurial process through performance management.

A more detailled description will be provided in January 2023 before the course is performed.

Lecturer: Prof. Dr. Klaus Möller
Semester: Spring semester
Credits: 4 ECTS

Leadership

Taking on leadership positions comes with a number of challenges: Leaders are expected to define a strategic framework for their team, department or overall organization, promote culture and collaboration and, above all, take responsibility for their employees. The importance of these leadership tasks is increasing heavily in many areas, especially in view of the increasingly dynamic, complex and uncertain work context.

Against this background, the seminar imparts central knowledge on the topic of leadership in organizations. In addition to basic knowledge and an overall organizational view of the topic, the seminar focuses in particular on modern concepts and current challenges in the leadership of employees and teams. In this context, on the one hand, the question will be addressed of how leadership actions in increasingly dynamic and flexible contexts can be designed in a contemporary and successful manner (e.g. through empowerment-oriented and shared forms of leadership). On the other hand, current megatrends are highlighted that represent central fields of action for leaders in organizations (e.g. dynamization and flexibilization of structures, digitalization, demographic change, mental health).

The topics covered in the seminar will be analysed on the basis of current scientific findings and deepened through practical examples and the processing of case studies.

Lecturers: Dr. Stefan Berger and Dr. Hendrik Hüttermann
Semester: Spring semester
Credits: 4 ECTS