[Lecture] Machine Learning
This course introduces students to the fundamental concepts, techniques, and algorithms in machine learning. It covers the mathematical and theoretical foundations, supervised and unsupervised learning techniques, evaluation methods, and advanced aspects. Students will gain hands-on experience in implementing, training, and optimizing machine learning models using real-world datasets.
The tentative list of topics is as followed:
- Introduction
- Probabilistic Inference
- Trees and Forests
- Neighbor-based methods
- Linear models
- (Convex) Optimization
- Gradient-based Optimization
- SVMs
- Kernels
- Basics of Deep Learning: MLPs, CNNs, GNNs
- Dimensionality Reduction: PCA & tSNE
- SVD & Matrix Factorization
- k-Means and GMMs
- Hierarchical Clustering
- Robustness
- Uncertainty
- Privacy
- Fairness
[Lecture] Introduction to Data Science and Machine Learning
Introduction to Data Science and Machine Learning
- The value of data from a business perspective
- Data quality and data cleaning
- Design of a data analysis process
- Explanation vs. prediction
- Data visualization
- Use of data to support entrepreneurial action
- Introduction to machine learning
- Programming language: Python
[Lecture] Mathematics of Data Science - An Introduction
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.
[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.
[Lecture] High Performance Computing for Machine Learning
High Performance Computing (HPC) is concerned with the efficient and fast execution of large simulations on modern supercomputers. It makes use of state-of-the-art technologies (such as GPUs, low-latency connections etc.) to efficiently solve complex scientific and data-driven problems. One of the key factors for the current success of machine learning models is the ability to perform calculations on modern computers with many model parameters and large amounts of training data. However, in their simplest form, current machine learning libraries only make limited efficient use of available HPC resources. The aim of this lecture is therefore to examine theoretical and practical aspects for the efficient training of machine learning and, in particular, deep learning models on modern HPC resources.
With this in mind, in the first part of the lecture, we will cover techniques that typically are used for the performance optimization of software on supercomputers. After a short introduction to HPC, we will deal specifically with GPUs (graphics processing units) and various memory models as well as performance optimization models and a practical introduction to CUDA, a programming interface developed by Nvidia for GPU programming.
In the second part of the lecture, the learnt techniques and concepts for the efficient training of Machine Learning and Deep Learning models will be applied. Different data- and model-parallel trainings methods for the efficient training on GPUs, algorithmic and practice-oriented, will be demonstrated using various examples from applications.
[Seminar] Limitations of Large Language Models
This seminar explores the critical limitations of Large Language Models (LLMs) through the study of:
Jailbreaking: How LLMs can be intentionally manipulated to bypass safeguards and restrictions, leading to unintended or unethical outputs. Hallucinations: The tendency of LLMs to generate confidently incorrect or fabricated information, undermining their reliability. Reasoning: Gaps in logical coherence and contextual understanding that affect the models' ability to perform consistent and accurate reasoning. Scalability: Challenges related to the increasing computational and environmental costs of training larger models, and the diminishing returns on performance improvements. We will also examine other aspects that underscore the limitations of LLMs, providing a comprehensive perspective on their current capabilities and future directions.
[Seminar] Machine Learning Methods to Predict Individual Differences With Python
This course will start with learning the basics of programming in Python, so no prior programming knowledge is needed, but helpful.
Next will be an introduction to data handling and visualization (i.e., with seaborn, matplotlib). After that, we will start with basic concepts used in machine learning (i.e., learning, prediction) and helpful methodologies (i.e., cross-validation to prevent overfitting).
In the final part of the course, we will learn to apply several machine learning procedures (i.e., regressions, tree models, deep-learning) and to use model explainers to allow the interpretation of results from otherwise black-box models. As software tools, we will use Jupyther/Colab notebooks. Miniconda or Anaconda should be installed, but all software is freely available. Depending on student demand, this course can be held in German.
[Seminar] Periodical Solutions in Mathematical Models for Neural Nets
As you read these lines, millions of neurons are generating electrical signals in your brain. The exchange – sending and receiving – of electrical signals between neurons creates vibrating nerve networks that perform complex oscillations. According to the latest findings in neuroscience, oscillations in brain activity play an important role in many of our brain's functions. They influence our attention, for example. Oscillations also play an important role in artificial neural networks. Artificial neural networks, which mimic networks of natural neurons, are successfully used in artificial intelligence. In this seminar we will get to know mathematical models for networks of artificial neurons with time-delayed interaction. The corresponding models consist of coupled nonlinear differential equations with time delay. Among other things, we will examine the problem of the existence and nonexistence of periodic solutions and the significance of negative coupling parameters in the emergence of oscillations.
[Seminar] Current Trends in Visualization
This seminar covers current research about the principles and for the application of information visualization in practice. Topics include the visual design of graphs, regressions, and hierarchical as well as temporal data, the connection between machine learning and visualization, interaction, perception, evaluation of visualization techniques or their application in practice.
[Seminar] Methods of Mathematical Modeling in Life Sciences
The seminar will discuss recent work on applications of mathematical modeling in the life sciences. The focus is on current developments of machine learning and artificial intelligence methods for industrial problems in the fields of pharmaceuticals and agricultural sciences. This seminar discusses different aspects, such as the mathematical methodology behind each method, its computational complexity, and possible applications. In individual cases, publicly available methods will also be applied and the results discussed.
[Seminar] Mathematical Foundations of the Natural Language Processing
This seminar deals with the mathematical basics of algorithmic language processing. The goal is to develop a solid understanding of methods used for processing natural language. If time permits, small applications from practice are demonstrated.
[Seminar] Seminar for Teachers at Grammar and Comprehensive Schools: AI Algorithms in Teaching
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.