Prof. Dr. Aleksandar Bojchevski, Institute of Computer Science

[Lecture] Introduction to Machine Learning

In the Introduction to Machine Learning course, we introduce the fundamental concepts, techniques, and algorithms for learning from data. We will cover both theoretical foundations and practical aspects of supervised and unsupervised learning, including modern neural networks and deep learning methods. Students will learn standard algorithms and models, understand how and when to apply them, and how to critically evaluate their performance. The hands-on exercises will reinforce the concepts.

Dr. Janine Weber-Hamacher, Mathematical Institute

[Lecture] Introduction to the Mathematics of Data Science

With the continuously growing importance and widespread use of automated simulations, decision-making processes, and AI, new challenges arise in the analysis and processing of data. In particular, the growing complexity of the tasks and the amount of data require new and more efficient approaches from the fields of data science, data mining, and machine learning in general.

In this lecture, theoretical and algorithmic principles of modern data processing and analysis will be covered. The lecture focuses mainly, but not exclusively, on the literature given below. Among other topics, the following will be studied:

• Techniques for dimension reduction (singular value decomposition / PCA / robust PCA)
• Classical regression
• Clustering algorithms
• Classification with Support Vector Machines and Linear Discriminant Analysis
• Classification with Classification Trees and Random Forest
• Classical Neural Networks and an introduction to Deep Learning
• Introduction to Reinforcement Learning
• Reduced Order Models (ROM)

The focus will be on the algorithmic and mathematical feasibility of the mentioned methods, an application-oriented implementation, and less on statistical methods, which are part of Data Science as well.

Brunton, S., & Kutz, J. (2022). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (2nd ed.). Cambridge: Cambridge University Press. doi:10.1017/9781009089517

Prof. Dr. Detlef Schoder, Faculty of Management, Economics and Social Sciences

[Lecture] Artificial Intelligence and Information Management

This course provides you with knowledge and skills required for assessing, managing, and deploying Artificial Intelligence (AI) for tasks of Information Management (IM). More recent advancements in data analytics and AI, e.g. Deep Learning, Natural Language Processing (NLP), Transformer models, and Convolutional Neural Networks, provide a powerful basis for data modeling, data analysis, and new services. The methodological approaches are deployable in many industries facing tasks of information management (IM). The course brings together both a technical and a managerial perspective. The technical perspective will cover (1) a general overview over Artificial Neural Networks (ANN or in short: NN) and the training process, (2) specific architectures like Convolutional Neural Networks and their application in Computer Vision (CV). (3) Natural Language Processing (NLP) including important concepts like Word Embeddings. Especially NLP-based approaches leveraging the Word Embedding concept will be of foremost interest including Transformer Models. Foundational prerequisites will be briefly revisited including types of data, feature selection, pre-processing of textual data, techniques for parameter optimization. The management perspective will cover selected topics at the interplay of AI and IM relevant for information managers including: 

•  AI Innovation: transform data into valuable information with an eye on data-based business model innovation
• Building organizational AI capability: identification, incorporation and development of necessary skillsets for managing AI and preparing an organization to become data-centric
• Ethics and AI, e.g., how to define and realize fair /un-biased use of data, algorithms and AI at large, aka Responsible AI, Explainable AI/ XAI
• AI and Automation – Future of Work: How work will be separated between man and machine in the future and how far can we get with AI in terms of automation?
• AI and Regulation: Is there need to regulate AI? How? 

Regulation and Systems Engineering The course strives for the state-of-the-art application of data analytics, AI approaches and issues in terms of information management. The course is less on mastering all theoretical underpinnings of the techniques or in the further development of the methods themselves. Rather, it is more on deploying AI and understanding the challenges of real-world problems. We will examine selected types of questions that can be treated with means of Artificial Intelligence and associated methods and tools. The emphasis is on understanding the concepts and logic behind a selected set of data analytics techniques. We will deploy a variety of flipped classroom elements, including team and lab work, small competitions and presentations. Individual and team assignments will be on provided data sets. Students will work in teams on a Kaggle-type competition.

Dr. Zoran Nikolić, Mathematical Institute

[Seminar] Transformer Models

The seminar is structured so that, in addition to the usual student presentations, practical programming assignments will be assigned. In the theoretical section, we will cover Transformer models with the attention mechanism. To this end, we will introduce neural networks as well as
embedding approaches. We will also learn about the method of Retrieval-Augmented
Generation (RAG). In the practical part, I will assign real-world tasks that will require writing Python notebooks with the corresponding API calls for GPT and RAG models.

Prof. Dr. Martin Schultz, Institute of Computer Science

[Seminar] Machine Learning for the Earth System

Topics are:

• Types of Earth system data
• Data formats and standards
• Earth science databases
• Principles of Earth system data management
• Working with time series data
• Working with gridded data
• Earth system metadata
• FAIR Earth system data
• Legal aspects, open data, and licensing
• Ethical aspects

Prof. Dr. Ulrich Trottenberg & Dr. Roman Wienands, Mathematical Institute

[Seminar] Seminar for teachers at grammar and comprehensive schools: Practical AI-algorithms for instruction

This seminar is targeted at student teachers who are interested in a realistic, youth-oriented teaching structure for the high-school level. It covers current algorithms used for Artificial Intelligence (AI) and Machine Learning (ML), specifically for regression and classification, different variants of neural networks, ChatGPT, Nearest Neighbor algorithm, algorithms based on decision trees, and more.

For the algorithms and mathematical models, teaching modules are supposed to be created that can supplement the current curricula. The lectures will present the required mathematical basics and a suitable didactic concept.