Search result: Catalogue data in Autumn Semester 2023
CAS in Applied Information Technology  The CAS takes place in Autumn Semester only. | ||||||||||||||||||||||||||||||||||||||||||||||||
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| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||
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| 265-0100-00L | Programming with Python  | O | 3 credits | 2A | L. E. Fässler | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | The initial module offers a practical introduction to some basic concepts and techniques for information processing as well as practical applications of them. The programming language are Python and SQL. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants learn... - how to encode a problem into a program, test the program, and correct errors. - to understand and improve existing code. - deal with the complexity of real data. - store data in a suitable data structure. - query databases and understand and evaluate the corresponding database model. - to implement mathematical models as a simulation. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | The following programming concepts are introduced during this module: 1. Variables, data types 2. Condition check, loops, logics 3. Sequential data types 4. Functions and Moduls 5. Data management (SQL) In the practical part of the course, students work on small programming projects with a context from natural sciences. Electronic tutorials are available as preparation. | |||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | No prior knowledge is required for this course. It is based on application-oriented learning. The students spend most of their time working through programming projects and discussing their results with teaching assistants. To learn the programming basics there are electronic tutorials available. | |||||||||||||||||||||||||||||||||||||||||||||||
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| 265-0101-00L | Data Science | O | 4 credits | 3V | B. Gärtner | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this module, basic paradigms and techniques in working with data will be discussed, especially towards data security, managing data decentrally, and learning from data. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants will understand some of the concepts in detail and see the mathematics behind them. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | The module in particular covers cryptography and digital signatures, networking and distributed algorithms, distributed ledger technology, as well as machine learning (supervised and unsupervised learning). For each topic, there will be a hands-on and in-depth introduction that allows participants to gain a technical understanding of key ideas. This is supported by simple and concrete examples as well as programming assignments. | |||||||||||||||||||||||||||||||||||||||||||||||
| 265-0102-00L | Computer Vision | O | 1 credit | 2V | E. Konukoglu | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | This module will cover basic theoretical knowledge on visual recognition systems of the last two decades, mostly focusing on the most recent advancements in deep learning and convolutional neural networks. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants understand basic concepts of visual regonition and human-computer interaction systems. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | The content starts with an introduction to neural networks and then focuses on how they are used for computer vision tasks. The theoretical knowledge will be supported with a practical session that will allow participants to gain hands-on experience with most commonly used tools and deepen their understanding of the key concepts with examples. | |||||||||||||||||||||||||||||||||||||||||||||||
| 265-0103-00L | Applied Information Technology | O | 4 credits | 3V | M. Brandis | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | This integration module links technical understanding of technology with business strategy based on a set of case studies from practice. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants will explore how new information technologies change different aspects of a business, and learn how to evaluate specific risks, costs, and benefits of such technologies. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | The module will shed light on success factors and common pitfalls when implementing new technologies and respective business changes, and it will specifically address the communication between technical experts and business management. The studied cases are currently planned to focus on artificial intelligence, IoT including edge and cloud computing, blockchain and distributed ledger technologies, and cybersecurity and data protection regulations (subject to change). | |||||||||||||||||||||||||||||||||||||||||||||||
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