Curriculum

As a student of our AIS program, you‘ll acquire all the skills necessary to see through AI projects from conception to implementation. This includes skills in the acquisition and pre-processing of data, the integration of ready-made AI components, as well as competence in customized machine learning models. Once an AI solution has proven to be functional, it will need implementation. This, in turn, requires competences in cloud computing and/or embedded systems, plus software development methods and project management, which you will also acquire on our course. Due to the practical orientation of the study program, you will futhermore become familiar with applying AI methods of computer vision and natural language processing in domains such as industry, medicine/healthcare, business and finance. Here is an overview of our curriculum and the content you can expect during the Artificial Intelligence Solutions program.

Modules

AI Methods

	1.	2.	3.	4.	6.
Introduction to Artificial Intelligence	5
Introduction to Artificial Intelligence Graduates will be familiar with the key subfields and application areas of AI methods, including a taxonomy of the various subfields of machine learning (supervised learning, unsupervised learning, and reinforcement learning). They will also be familiar with the basics of the history of artificial intelligence. Graduates will be able to solve standard tasks such as character classification using existing software (e.g., pre-built notebooks or no-code solutions such as Knime, RapidMiner, or HeuristicLab). Furthermore, they will be familiar with the application possibilities and limitations of existing large language models (LLMs) and the chatbots based on them, such as ChatGPT, and will be able to solve simple tasks with them. Introduction to Artificial Intelligence Semester: 1 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Lecture part: definition of Artificial Intelligence; taxonomy and history of AI, narrow and strong AI; current applications of AI, prospective future applications of AI; the role of machine learning in AI, taxonomy of machine learning; overview of symbolic AI; overview of heuristic optimization and symbolic regression. Practical part: low-threshold hands-on workshops how to use notebook templates and no-code solutions (Knime, HeuristicLab) for solving simple machine learning tasks; practical usage of Large Language Models and chatbots. Introduction to Artificial Intelligence Semester: 1 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Lecture part: definition of Artificial Intelligence; taxonomy and history of AI, narrow and strong AI; current applications of AI, prospective future applications of AI; the role of machine learning in AI, taxonomy of machine learning; overview of symbolic AI; overview of heuristic optimization and symbolic regression. Practical part: low-threshold hands-on workshops how to use notebook templates and no-code solutions (Knime, HeuristicLab) for solving simple machine learning tasks; practical usage of Large Language Models and chatbots.
Heuristic Optimization			5
Heuristic Optimization Graduates will have a solid understanding of methods for building mathematical models for various types of systems (continuous and discrete) and methods and algorithms for solving optimization problems in various domains. They will acquire knowledge of methods and heuristic algorithms for solving data analysis and optimization problems in different domains, or knowledge of methods and algorithms of classical (numerical) methods for solving optimization problems in the same domains. Heuristic Optimization and Symbolic Regression Semester: 3 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Taxonomy of system models; taxonomy of optimization: differentiation be-tween numerical and heuristic optimization, examples of combinatorial opti-mization problems; complexity theory: solution space behavior, P and NP problems; heuristic methods: problem-specific methods vs. metaheuristics, construction vs. improvement heuristics, proximity and distance of solutions, local search; trajectory-based methods: simulated annealing, tabu search; population-based methods: ant-colony optimization, swarm intelligence, ge-netic algorithms, evolution strategies, genetic programming, scatter search.v Heuristic Optimization and Symbolic Regression Semester: 3 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Taxonomy of system models; taxonomy of optimization: differentiation be-tween numerical and heuristic optimization, examples of combinatorial opti-mization problems; complexity theory: solution space behavior, P and NP problems; heuristic methods: problem-specific methods vs. metaheuristics, construction vs. improvement heuristics, proximity and distance of solutions, local search; trajectory-based methods: simulated annealing, tabu search; population-based methods: ant-colony optimization, swarm intelligence, ge-netic algorithms, evolution strategies, genetic programming, scatter search.v
Reinforcement Learning					2.5
Reinforcement Learning Graduates will be able to identify problems that can be effectively solved using reinforcement learning and apply these methods to simple practical scenarios. They will also acquire fundamental skills and concepts that will enable them to understand more complex approaches in this field from a higher-level perspective. Reinforcement Learning Semester: 6 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung This course introduces Reinforcement Learning. Basic concepts such as Markov Decision Processes, Dynamic Programming, and Value Functions are presented and explained through practical examples. The course also covers main algorithms and methods, including Monte Carlo and Temporal Difference techniques. These are analyzed and implemented in a series of exercises using Python notebooks. Applications involving Deep Neural Networks are also explored.
Supervised Machine Learning		5
Supervised Machine Learning Graduates understand the basic concepts of supervised machine learning, in particular, the concept of generalization error and how it can be estimated in practical tasks. They know the essential methods for evaluating and assessing machine learning models and can also apply these methods in practice. They understand the sources of error (underfitting and overfitting) and can recognize and avoid them in practical tasks. Graduates know the essential methods (excluding artificial neural networks, which are covered in another course module), can decide which methods are suitable for which tasks, and can apply these methods in practical tasks. Supervised Machine Learning Semester: 2 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Introduction to supervised machine learning, classification, and regression; joint distribution of inputs and outputs, generalization error; estimation of generalization error using training and test sets; cross validation; confusion tables and evaluation measures derived from them; evaluation measures for unbalanced classification tasks; receiver-operator characteristics curve; evaluation measures for regression; underfitting and overfitting; hyperparameter optimization; supervised machine learning methods: k-nearest neighbor, linear regression, support vector machines, decision trees, tree ensembles: bagging (random forests) and boosting. Extensive practical exercises deepen the subjects of the lecture through practical examples. Supervised Machine Learning Semester: 2 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Introduction to supervised machine learning, classification, and regression; joint distribution of inputs and outputs, generalization error; estimation of generalization error using training and test sets; cross validation; confusion tables and evaluation measures derived from them; evaluation measures for unbalanced classification tasks; receiver-operator characteristics curve; evaluation measures for regression; underfitting and overfitting; hyperparameter optimization; supervised machine learning methods: k-nearest neighbor, linear regression, support vector machines, decision trees, tree ensembles: bagging (random forests) and boosting. Extensive practical exercises deepen the subjects of the lecture through practical examples.
Unsupervised Machine Learning			5
Unsupervised Machine Learning Graduates will understand the fundamental concepts of unsupervised machine learning and the basic method classes such as clustering, association rule learning, projection methods, and generative modeling. They will be familiar with the essential methods of these method classes and be able to apply them in practice. Unsupervised Machine Learning Semester: 3 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Introduction to unsupervised machine learning and its applications. Cluster-ing: agglomerative clustering, k-means and k-medoid clustering, affinity prop-agation clustering, density-based clustering. Mining frequent item sets and association rules; the A-Priori algorithm. Projection methods: principal com-ponent analysis, independent component analysis, projection pursuit, t-SNE, UMAP. Basic idea of generative modeling and latent-variable models. Extensive practical exercises deepen the subjects of the lecture through practical examples. Unsupervised Machine Learning Semester: 3 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Introduction to unsupervised machine learning and its applications. Cluster-ing: agglomerative clustering, k-means and k-medoid clustering, affinity prop-agation clustering, density-based clustering. Mining frequent item sets and association rules; the A-Priori algorithm. Projection methods: principal com-ponent analysis, independent component analysis, projection pursuit, t-SNE, UMAP. Basic idea of generative modeling and latent-variable models. Extensive practical exercises deepen the subjects of the lecture through practical examples.
Neural Networks and Deep Learning			5
Neural Networks and Deep Learning Graduates understand the essential concepts and architectures of artificial neural networks (including convolutional neural networks and recurrent networks) and are able to design and train neural networks suitable for practical tasks and critically evaluate the results achieved. Neural Networks and Deep Learning Semester: 3 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Basic idea of artificial neural networks: neurophysiological basics, function of a nerve cell, perceptrons, the perceptron learning algorithm, linear separabil-ity. Multi-layered networks and the backpropagation algorithm, online, batch, and minibatch training, tricks of the trade, regularization. The vanishing gra-dient problem; deep networks, alternative activation functions, dropout. Con-volutional neural networks: basic idea and architecture, convolutional filters, pooling, flattening, training of convolutional networks, fully convolutional net-works. Recurrent neural networks, the vanishing gradient problem revisited, long short-term memory cells and networks, gated recurrent units. Extensive practical exercises deepen the subjects of the lecture through practical examples. Neural Networks and Deep Learning Semester: 3 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Basic idea of artificial neural networks: neurophysiological basics, function of a nerve cell, perceptrons, the perceptron learning algorithm, linear separabil-ity. Multi-layered networks and the backpropagation algorithm, online, batch, and minibatch training, tricks of the trade, regularization. The vanishing gra-dient problem; deep networks, alternative activation functions, dropout. Con-volutional neural networks: basic idea and architecture, convolutional filters, pooling, flattening, training of convolutional networks, fully convolutional net-works. Recurrent neural networks, the vanishing gradient problem revisited, long short-term memory cells and networks, gated recurrent units. Extensive practical exercises deepen the subjects of the lecture through practical examples.
Symbolic AI		5
Symbolic AI Graduates are familiar with the formal calculus underlying symbolic AI systems: predicate logic for knowledge representation and inference in deterministic models, as well as concepts of multidimensional random variables for stochastic models. They can independently solve symbolic AI tasks, such as, planning or searching in game situations, by applying standard algorithms. In the area of stochastic systems, they are familiar with graphical models and can use them to solve inference problems. Logic and Symbolic AI Semester: 2 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Logic as the language of science: propositional and predicate logic, knowledge representation, entailment vs. inference, soundness and completeness. Elementary AI algorithms: search (including informed search, game search and constraint satisfaction) and planning. Symbolic representation of uncertainty: Joint distributions of random variables, Bayesian networks, hidden Markov models to Markov reward and Markov decision processes and foundations of reinforcement learning. Exercises deepen the subjects of the lecture through practical examples. Logic and Symbolic AI Semester: 2 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Logic as the language of science: propositional and predicate logic, knowledge representation, entailment vs. inference, soundness and completeness. Elementary AI algorithms: search (including informed search, game search and constraint satisfaction) and planning. Symbolic representation of uncertainty: Joint distributions of random variables, Bayesian networks, hidden Markov models to Markov reward and Markov decision processes and foundations of reinforcement learning. Exercises deepen the subjects of the lecture through practical examples.
Generative AI				5
Generative AI Graduates will know and understand the essential deep learning technologies behind current generative AI systems, such as (variational) autoencoders, generative adversarial networks, transformers, and stable diffusion networks. They will be able to design and train simple variants themselves. Generative AI Semester: 4 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Principles of generative modeling in detail and how to perform generative modeling with deep learning. Autoencoders, variational autoencoders. Generative Adversarial Networks: architectures, training, technical issues. Transformers: architectures and training, sequence-to-sequence learning, applications to natural language processing, vision transformers. Stable diffusion networks, Contrastive Language-Image Pre-training. Extensive practical exercises deepen the subjects through practical examples. Generative AI Semester: 4 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Principles of generative modeling in detail and how to perform generative modeling with deep learning. Autoencoders, variational autoencoders. Generative Adversarial Networks: architectures, training, technical issues. Transformers: architectures and training, sequence-to-sequence learning, applications to natural language processing, vision transformers. Stable diffusion networks, Contrastive Language-Image Pre-training. Extensive practical exercises deepen the subjects through practical examples.

AI Applications

	3.	4.	6.
AI Technologies	2.5	10
AI Technologies Graduates will know and understand the tasks that arise in the application areas of time series analysis, computer vision, and natural language processing. They will be able to select the most suitable method for the respective task and, if necessary, use ready-made software components and configure/adapt them for the given task in order to solve the problem. In the area of time series analysis, graduates will be familiar with the particular challenges and risks of predicting non-stationary time series and will be able to assess and evaluate them for a specific task. Graduates will be able to analyze and process images and videos to extract information and solve visual tasks. They will understand the basic concepts of image processing and computer vision, such as filtering, segmentation, feature extraction, and object detection. They will be able to apply and adapt various algorithms and techniques for processing images and videos to develop specific applications in different domains. Furthermore, they will also be able to assess and evaluate the challenges and limitations of different methods. Graduates will be able to solve standard tasks in the field of natural language processing, such as text classification, information extraction, and clustering. They will also be familiar with the applications and limitations of existing large language models (LLMs) and the chatbots based on them, such as ChatGPT. Based on frameworks for large language models, graduates will be able to develop applications for practical tasks. Time Series Analysis Semester: 3 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung preprocessing of time series data. Stationary processes and ARMA models. Spectral analysis. Nonstationary and seasonal time series models. Statespace models. Machine learning models for time series forecasting. Anomaly detection in time series. Using frameworks like ‘prophet’ and ‘merlion’. Special challenges in sales forecasting and forecasting of financial time series. Extensive practical exercises deepen the subjects through practical examples. Computer Vision Semester: 4 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 5 Exam type: Immanente Beurteilung Fundamentals of digital image processing and computer vision: human perception, images and their representations, color models, image statistics, linear filters and their applications. Theoretical and practical aspects when working with digital images. Techniques and datasets for image classification, segmentation, object and keypoint detection. Training, transfer-learning and usage of pre-trained models such as neural networks, convolutional neural networks (CNNs) and transformers. Data preparation and image augmentation techniques. Current trends in computer vision. Extensive practical exercises to deepen the understanding of the topics covered. Natural Language Processing Semester: 4 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 5 Exam type: Immanente Beurteilung Building a simple language model from scratch. Creating practical applications based on language models. Building your own "ChatGPT Clone". Document Question and Answering. Introduction to the Langchain Framework.
Electives: Domain-Specific AI Applications			7.5
Electives: Domain-Specific AI Applications These courses consist of an overview of the specific features of the respective domain in terms of data, guidelines, etc., while the majority of the courses consist of case studies of specific AI applications within the respective domain. This enables graduates to master the specific challenges of their chosen application domains and to apply their methodological knowledge and data understanding in such a way that a suitable and viable solution for a specific task can be achieved that complies with the practices and guidelines of the respective domain. AI in Business and Finance Semester: 6 Type: Wahl, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Special challenges of finance and business domains: non-stationary time series, biased data, changing market regimes, high dimensionality, overfitting issues even if the number of parameters of a model is low, etc. Case studies include, but need not be limited to: AI-based lead generation, lead scoring, opportunity scoring, sales forecasting, recommender systems and AI-based guided selling, customer analytics, AI-based scoring and trading of securities and commodities. AI in Industry 4.0 Semester: 6 Type: Wahl, Vorlesung ECTS: 2,5 Exam type: Schriftliche Prüfung Special challenges of industrial applications: amount of data, timing issues, asynchronous data from IoT sensors, reliability and safety of models, model quality, unknown influences. Case studies include, but need not be limited to: modeling and optimization of industrial processes, condition monitoring and predictive maintenance, industrial quality control, monitoring and prediction. AI in Medicine and Healthcare Semester: 6 Type: Wahl, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Special challenges of medicine and healthcare: lack of data, missing values, privacy and regulation issues, ethical questions, biased data (i.e. stratification issues), unknown influences. Case studies include, but need not be limited to: risk assessment, outcome prediction, medical imaging diagnostics, robotassisted surgery, virtual health assistants.

Software Development and Architectures for AI

	1.	2.	3.	4.	5.	6.
Basics of Programming	5
Basics of Programming Graduates understand the fundamental elements of programming, such as elementary data types, control structures, and functions/procedures, and how they can be used in an interpreted, dynamically typed programming language such as Python. They can implement simple algorithms in Python. Furthermore, they understand the basic concepts of object-oriented programming (classes, inheritance, polymorphism) and how they can be represented in Python. Graduates know the basic functionality of the numpy library and can implement simple numerical algorithms using this library in Python. Graduates can write and execute Python programs using a simple development environment (PyCharm). They also know the concept of notebooks (Jupyter or Google Colab) and can use them for exploratory program development. Object-Oriented Programming With Python Semester: 1 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 5 Exam type: Immanente Beurteilung Elementary data types, control structures and functions/procedures; Python as a universal, dynamically typed scripting language; Python development environments (PyCharm) and notebooks (Jupyter / Google Colab); using the most important elements of the Python standard library; basics of object-oriented programming (classes, inheritance, polymorphism) and their realization in Python; the program library 'numpy' and its use to implement simple numerical algorithms. Lectures will be interwoven with practical work and assignments to practice the acquired knowledge.
Software Development		5
Software Development Graduates have detailed knowledge of modern project development methods that focus on customers and quality. That particularly includes agile approaches. They are also familiar with maintaining product quality throughout the entire product lifecycle (application lifecycle management). Graduates also know the motivation and basic concepts of the DevOps/MLOps culture and can automate the process of creating, testing and delivering software in general and machine learning models, in particular, using various tools. DevOps and MLOps Semester: 2 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Motivation and basic concepts of the DevOps/MLOps culture, process of software deployment (continuous deployment), version control and CI/CD systems, machine learning workflow (data preparation/training/test/deployment) and model lifecycle management, methods and tools for automating the ML workflow, definition of CI/CD pipelines on different platforms, command line interfaces and scripting, container management, monitoring and observability. IT Project Management Semester: 2 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Project management, project calculation, key figures and controlling, document management, agile project management, quality management and development, quality models, risk treatment, risk management, product life cycle management, basic process terms, modern development processes (including documents, rolls, tools).
Distributed Software Architectures			7.5
Distributed Software Architectures Graduates will possess knowledge of threats, objectives, and technical measures in the field of information security. They will be familiar with the fundamentals of protection objectives and their definition, as well as methods for creating threat models and the necessary technical foundations in the areas of computer network security and internet/cyber security. They will be able to apply cryptographic procedures to achieve security objectives and can specifically utilize technologies for authentication, access control, and information flow control. They will be able to evaluate technologies and products for their suitability for achieving security objectives. Graduates will be familiar with the challenges and problems of big data regarding indexing, scalability, and processing across a wide range of resource-intensive settings, big data components, big data stream techniques, and algorithms that enable the analysis of large amounts of data in streaming environments. Students will be able to design appropriate big data architectures for large data volumes with the goal of high-performance processing. You will be able to use current tools and components such as Hadoop (MapReduce, HDFS, Spark, etc.) and NoSQL databases to process large amounts of data and extract valuable knowledge. Data and Information Security Semester: 3 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Introduction to the basics of information security and its terms, protection goals as well as their definition. Analysis of threats and creation of threat models, as well as the acquisition of the necessary basics in the areas of computer networks and their security, nternet/cybersecurity, system security and the application of cryptographic methods to achieve security goals. Authentication and access/access control technologies, privacy enhancement techniques, and secure systems engineering fundamentals. Software Architectures for Big Data Semester: 3 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Introduction to Big Data (Challenges/Impact of Big Data on data processing, sources of Big Data, e.g. Social Media, sensory networks, geographical data, traffic data, surveillance data, weather data); Theoretical foundations of Big Data architectures (Lambda, Kappa); Basics of application deployment in Big Data environments with containers (Docker), orchestration software (Kubernetes) and cloud providers (Azure); NoSQL databases for Big Data applications (e.g. Redis, Cassandra, MongoDB); Big Data Stacks like Apache Hadoop and Microsoft Azure; Application of tools and Big Data Components like MapReduce, HDFS, Spark, Airflow; Stream processing tools (e.g. Spark Streaming, Kafka); Example applications: recommender systems, analysis of social network graphs, sentiment analysis, opinion mining. Software Architectures for Big Data Semester: 3 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Introduction to Big Data (Challenges/Impact of Big Data on data processing, sources of Big Data, e.g. Social Media, sensory networks, geographical data, traffic data, surveillance data, weather data); Theoretical foundations of Big Data architectures (Lambda, Kappa); Basics of application deployment in Big Data environments with containers (Docker), orchestration software (Kubernetes) and cloud providers (Azure); NoSQL databases for Big Data applications (e.g. Redis, Cassandra, MongoDB); Big Data Stacks like Apache Hadoop and Microsoft Azure; Application of tools and Big Data Components like MapReduce, HDFS, Spark, Airflow; Stream processing tools (e.g. Spark Streaming, Kafka); Example applications: recommender systems, analysis of social network graphs, sentiment analysis, opinion mining.
Embedded Software Development				5	2
Embedded Software Development Graduates will be familiar with the basic steps in the design process for developing AI models for embedded systems. They will understand the relationships between microcontroller architecture, their limited resources, and their impact on model execution under real-time conditions. Students will be familiar with converting models from high-level languages such as Python to C or C++, metric-based optimization and adaptation to hardware architectures, and concepts for subsequently training models directly on embedded systems. Graduates will acquire fundamental knowledge of the tasks and methods of requirements engineering. They will be familiar with basic principles, standards, and various methods in traditional and agile requirements engineering. They will be able to select and apply these methods to systematically determine software requirements from customer needs, describe them in the necessary level of detail, communicate them to the team, and manage them throughout the project. Furthermore, they will be able to evaluate the risks and benefits of requirements effort. Embedded AI Semester: 4 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 5 Exam type: Immanente Beurteilung Optimization of models for processing time series data using performance metrics and automated benchmarking tools, in detail the interaction between hardware architecture and optimization methods. Conversion of models using standard tools and libraries. Introduction to methods for retraining and online learning directly on embedded systems. Accompanying exercises and project work in which the entire development pipeline is implemented in detail. Requirements Engineering Semester: 5 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Fundamentals of requirements engineering, methods for requirements elicitation (e.g. contextual inquiry, interviews, checklists), documentation methods (e.g. UML, use cases, software requirements specification) and standards (e.g. ISO/IEC/IEEE 29148), quality criteria for requirements, review techniques, agile requirements engineering (e.g. impact map, user stories, backlog), requirement engineering tools, application-life cycle, nonfunctional requirements.
Advanced Software Engineering for AI Systems						5
Advanced Software Engineering for AI Systems Graduates will have an overview of key methods of user experience design with a focus on AI applications. They will go through the user-centered design process in a practical project that addresses topics from the "Sustainability and Ethics" module. They will be familiar with various prototyping techniques and will be able to design an AI project with user interests in mind, incorporating interaction concepts. They will also be familiar with the key user-relevant properties of intelligent interactive systems (such as conversational AI, XAI, recommender systems, etc.) and will be able to improve their usability or design new and similar systems. Furthermore, they will be able to measure and evaluate interaction with AI systems. Graduates will be able to understand the different requirements of information security, operational safety, and privacy. They will be able to identify and assess the key security and data protection risks in and through machine learning applications and the data used, and develop fundamental solution concepts. The need for Explainable Artificial Intelligence (XAI) for critical security and safety applications is understandable, and graduates can distinguish between the concepts of transparency and interpretability. Based on selected practical machine learning use cases, awareness of security, safety, and privacy is heightened, and graduates are thus able to professionally classify current technologies and developments in the field. Security and Safety Aspects of AI Solutions Semester: 6 Type: Pflicht, Vorlesung ECTS: 2,5 Exam type: Immanente Beurteilung Security, safety and privacy requirements for AI and machine learning, licensing risks, security impact of data quality and hidden bias, introduction to model inversion and its privacy implications, introduction to explainable AI, transparency and post-hoc explainability for safety applications, example: sensitive data handling in face recognition, example: audio/video deep fakes, example: security support systems using Large Language Models (LLMs). User Experience und Intelligent User Interfaces Semester: 6 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Principles of human-centered design and user experience with a dedicated focus on AI systems and intelligent user interfaces. Components of UX design (frameworks, guidelines), tools and methods (prototyping, evaluation methods) to design interactive, intelligent systems. User experience evaluation and principles of intelligent systems such as conversational AI, recommender systems, explainable artificial intelligence, human-AI collaboration, robotic and ubiquitous smart systems.

Computer Science

	1.	3.
Foundations of Computer Science	5
Foundations of Computer Science Graduates possess a variety of basic skills that are essential in an IT environment: On the one hand, they possess knowledge of the fundamental facts of computer science, meaning they are familiar with computer architecture, operating systems, and computer networks at a beginner's level. They have a basic understanding of the components of information-processing systems such as computers, tablets, or smartphones, how they function at the hardware level, the role of operating systems in such systems, and how data transmission works in networks. On the other hand, they are familiar with the methodology of computer science (computational thinking) in the sense that they can abstract and model at a beginner's level, as well as apply iterative, algorithmic approaches as technical-scientific problem-solving strategies (at least in basic terms). Foundations of Computer Science Semester: 1 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 5 Exam type: Immanente Beurteilung Computer architecture: components of a computer and their interaction, von Neumann architecture, processor, main memory, buses, storage media, input and output devices. Operating systems: processes, threads, scheduling, synchronization, memory management, file management, device management, command line; Networks: ISO/OSI model, hubs, switches, routers, TCP/IP, application layer protocols: DNS and HTTP. Computational thinking: problem analysis, problem decomposition and abstraction, recognition of similarities in problems (pattern recognition), modelling, formal problem solving and creation of algorithms, automation of solution steps. Lectures will be interwoven with practical work and assignments to practice the acquired knowledge.
Algorithms and Data Structures	5
Algorithms and Data Structures Graduates will be familiar with the most important operations on static and dynamic data structures. They will be familiar with the most important elementary algorithms, especially for searching and sorting, as well as the concept of recursion. They will be familiar with pseudo-random numbers and their generation, evaluation, and application. Graduates will be able to specify, design, and implement algorithms and analyze their complexity in terms of structure, memory requirements, and runtime. Algorithms and Data Structures Semester: 1 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Standard operations on static data structures (arrays), creation and management of dynamic data structures (single and doubly linked lists and various types of trees) and standard operations on them. Elementary algorithms for searching (sequential and binary search) on arrays, linked lists and binary (search) trees. Concept of recursion, recursive algorithms and their process, transformation from recursive to iterative algorithms and vice versa. Sorting algorithms (selection and insertion sort, shell sort, exchange sort and quicks-sort), their application and analysis. Concept of pseudo-random numbers, generators for and properties of random number sequences as well as example for the applications of random numbers. Specifying and designing algorithms from a functional point of view (method of incremental refinement). Structure, memory and runtime complexity of algorithms and analysis of algorithms with regard to these types of complexity. Exercises deepen the subjects of the lecture through practical examples. Algorithms and Data Structures Semester: 1 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Standard operations on static data structures (arrays), creation and management of dynamic data structures (single and doubly linked lists and various types of trees) and standard operations on them. Elementary algorithms for searching (sequential and binary search) on arrays, linked lists and binary (search) trees. Concept of recursion, recursive algorithms and their process, transformation from recursive to iterative algorithms and vice versa. Sorting algorithms (selection and insertion sort, shell sort, exchange sort and quicks-sort), their application and analysis. Concept of pseudo-random numbers, generators for and properties of random number sequences as well as example for the applications of random numbers. Specifying and designing algorithms from a functional point of view (method of incremental refinement). Structure, memory and runtime complexity of algorithms and analysis of algorithms with regard to these types of complexity. Exercises deepen the subjects of the lecture through practical examples.
Advanced Programming		5
Advanced Programming Graduates will understand the functionality of compiled programming languages, using C and C++ as examples. They will master the basic syntax rules, structures, data types, and control structures in C and C++. They will be familiar with the concepts of memory management, pointer arithmetic, hardware access, and design patterns for hardware-related programming. Graduates will understand the design process for software development for embedded systems, as well as basic error handling and debugging methods. Programming in C and C++ Semester: 3 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 5 Exam type: Immanente Beurteilung Introduction to compilable languages, compiler directives, make files, the basic language specifics of C and C++, elementary I/O functions, error handling and debugging, and basic concepts of object orientation. Introduction to system architecture and real-time capabilities of microcontrollers and software development for embedded systems. Utilization of peripherals (e.g., sensors and actors), interfaces for communication, libraries, design patterns for embedded systems and interrupts.

Applied Mathematics

	1.	2.
Linear Algebra and Calculus	5
Linear Algebra and Calculus Graduates will understand the fundamentals of vector and matrix calculus and how they can be used to solve problems such as solving systems of linear equations. Graduates will also understand the basic concepts of one- and multi-dimensional real-valued functions, as well as the fundamentals of differential and integral calculus in one or more variables. After completing this module, graduates will be able to understand the mathematical foundations of machine learning methods such as linear and logistic regression and the backpropagation algorithm in advanced modules. Basics of Linear Algebra and Calculus Semester: 1 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Linear Algebra: vectors and vector spaces, linear functions, matrices, determinants, rank of a matrix, linear systems, inverse matrix, dot product, orthogonality, eigenvalues and eigenvectors. Calculus: limits, continuity, derivatives, basic differentiation rules, partial derivatives, gradients, vector differentiation; Riemann integral and stem functions, basic integration rules. Exercises deepen the subjects of the lecture through practical examples. Basics of Linear Algebra and Calculus Semester: 1 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Linear Algebra: vectors and vector spaces, linear functions, matrices, determinants, rank of a matrix, linear systems, inverse matrix, dot product, orthogonality, eigenvalues and eigenvectors. Calculus: limits, continuity, derivatives, basic differentiation rules, partial derivatives, gradients, vector differentiation; Riemann integral and stem functions, basic integration rules. Exercises deepen the subjects of the lecture through practical examples.
Probability and Statistics		5
Probability and Statistics Graduates know the essential concepts of probability theory, distributions, and multidimensional joint distributions. They can apply these concepts to practical tasks and are able to understand the basics of machine learning in subsequent courses (e.g. understanding a prediction function in supervised machine learning as a model of a conditional distribution; understanding the generalization error as the expected value of the error with respect to the distribution of the data). Graduates know the essential concepts of descriptive statistics and can apply them to evaluate data and machine learning results. They know the basic principles of statistical estimation and are able to understand modeling and evaluation in machine learning as estimates in subsequent course modules. Graduates know the basic principle of statistical hypothesis testing, know the most important tests, and are able to apply them correctly in simple practical tasks. Basics of Probability and Statistics Semester: 2 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Probability theory: random experiments and probability, combinatorics, conditional probability and Bayes rule, random variables, expectation and variance, discrete distributions, joint distributions, (conditional) independence, continuous distributions, normal distribution and the central limit theorem. Basics of descriptive statistics Inferential statistics: estimators and their properties, confidence intervals, basic concepts of hypothesis testing with binomial test and t-tests as examples. Exercises deepen the subjects of the lecture through practical examples. Basics of Probability and Statistics Semester: 2 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Probability theory: random experiments and probability, combinatorics, conditional probability and Bayes rule, random variables, expectation and variance, discrete distributions, joint distributions, (conditional) independence, continuous distributions, normal distribution and the central limit theorem. Basics of descriptive statistics Inferential statistics: estimators and their properties, confidence intervals, basic concepts of hypothesis testing with binomial test and t-tests as examples. Exercises deepen the subjects of the lecture through practical examples.

Data Management and Data Processing

	1.	2.	3.	4.	5.	6.
Data Management		5
Data Management Graduates are able to use relational database management systems and implement data models. They know the query language SQL and can create relational database models for typical transaction-oriented applications. In addition, graduates know the special requirements for database systems for analytical data processing and know the special concepts and methods for data warehousing. Graduates can design dimensional data models and know implementation variants for dimensional data models. Databases and Data Warehouses Semester: 2 Type: Pflicht, Vorlesung ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Introduction to databases and advantages of using database systems, basic concepts (data model, scheme, instances) and components of database systems, architectures of database systems and data independence, basics of modeling (model concept, techniques and methods), database models; entity-relationship model, relational model and relational query models (relational algebra, query and tuple calculus), phases of database design (conceptual, logical, physical design), relational database design (functional dependencies, normal forms, transformation properties), basics of database definition and database queries with SQL. Analytical vs. transactional data processing – different architectures for different requirements, data warehouse (DWH) as a unified source of record for analytical data, application examples for data warehouse systems and DWH architectures. Conceptual modeling: dimensional fact model according to Golfarelli. Implementation of dimensional data models on RDBMS: star schema & snowflake schema. Data integration: data vault schema. Extract-Transform-Load process (ETL). Technological concepts for data warehousing: bitmap index, column store, compression, in-memory. Databases and Data Warehouses Semester: 2 Type: Pflicht, Übung ECTS: 3 Exam type: Immanente Beurteilung Introduction to databases and advantages of using database systems, basic concepts (data model, scheme, instances) and components of database systems, architectures of database systems and data independence, basics of modeling (model concept, techniques and methods), database models; entity-relationship model, relational model and relational query models (relational algebra, query and tuple calculus), phases of database design (conceptual, logical, physical design), relational database design (functional dependencies, normal forms, transformation properties), basics of database definition and database queries with SQL. Analytical vs. transactional data processing – different architectures for different requirements, data warehouse (DWH) as a unified source of record for analytical data, application examples for data warehouse systems and DWH architectures. Conceptual modeling: dimensional fact model according to Golfarelli. Implementation of dimensional data models on RDBMS: star schema & snowflake schema. Data integration: data vault schema. Extract-Transform-Load process (ETL). Technological concepts for data warehousing: bitmap index, column store, compression, in-memory.
Data Processing		5
Data Processing Graduates know the essential methods for preprocessing data for advanced machine learning, in particular, scaling and transformation, as well as treatment and imputation of missing values. They can implement these in Python using the 'pandas' package to solve practical tasks. Graduates are aware of the importance of visualization for understanding and interpreting data, they can classify data sources and types, and know appropriate types of visualization. They can design visualizations so that they correspond to human visual perception. Graduates can further apply the most important models and steps for the process of information visualization to their own tasks. They are able to create relevant visualizations for a selected data set using visualization tools in order to identify characteristic patterns, outliers or trends. Data and Information Visualization Semester: 2 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung The course introduces the essential contents of interactive information visualization. It is explained, 1.) where the added value of information visualization lies, 2.) to what extent visualizations can exploit human perception to make patterns, trends, and outliers in abstract data visible, 3.) how visualizations help memory and cognition, 4.) which cognitive and perceptual limits information visualization has, and 5.) which central role interaction plays in information visualization. This theoretical content is applied in practice and deepened in the practical part of the course by interactively visualizing a wide variety of data sets with Python visualization libraries (e.g., Seaborn, Altair) and the visualization tools (e.g. Tableau and Microsoft Power BI) in order to identify interesting patterns or trends in them. The technological implementation and the user-friendly design of the visualizations are evaluated. Data Quality and Data Preprocessing Semester: 2 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Introduction to data: basic introduction and concepts; taxonomy of data; data representation; summarization and exploratory analysis; distance and similarity; example: central limit theorem. Data preprocessing: the data engineering pipeline; wrangling, cleaning, preprocessing; data quality; descriptive data summarization: basic statistics, skewness, dispersion, outliers; the box plot; missing values: sources (missing at random, missing not at random, not missing at random), mitigation strategies, missing values imputation; noise and noise removal; binning and scaling; basics of principal component analysis and linear discriminant analysis. All concepts and methods will be practiced in the exercise part of the course based on Python and the ‘pandas’ library.

Complementary Competences

	1.	5.	6.
Soft Skills	5
Soft Skills Graduates are able to integrate different ethical theories into their respective contexts. They have basic knowledge of AI ethics and can recognize and produce trustworthy AI. They are able to use AI tools to develop new ideas, visualize them, communicate them to others, and evaluate them in relation to the respective challenge. Graduates are able to develop new ideas, visualize them, communicate them to others, and evaluate them in relation to the problem. They are able to successfully plan, design, and deliver professional concepts and presentations. Creative Techniques and Presentation Semester: 1 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2 Exam type: Immanente Beurteilung Learning of creative methods and techniques as well as planning and analysis methods to increase your own creativity as well as to improving creative teamwork for a successful implementation of idea generation processes. Acquisition of skills for the generation, visualization and communication of new ideas and concepts relevant for the target group, for the planning and implementation of innovation strategies and processes as well as methods for selecting proposed solutions and evaluation procedures. Creation and visualization of infographics, concepts, presentations, and training materials. Ethics and Trustworthy AI Semester: 1 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 3 Exam type: Immanente Beurteilung This integrated lecture conveys the fundamentals of digital ethics and responsible AI. Among others, it embraces issues of privacy, dignity, AI for good and the SDGs. Against the background of artificial intelligence, ethical theories are discussed and critically reflected. AI tools are explored to create a digital output which is then subjected to a fact check and source research to stimulate creative and critical thinking.
Scientific Skills		3
Scientific Skills Data storytelling represents the last mile in the implementation of a data science project within a company. Graduates are able to communicate data science projects in a target-group-appropriate manner to decision-makers, subject matter experts, and users in the company's specialist departments in such a way that their complex project can be understood by laypeople, decisions can be made, and business-related measures can be derived from it. Graduates can translate their expertise into simple language, thus making their topic accessible to a broad audience. Graduates are able to process and research a scientific topic, are familiar with the main types of scientific literature, and are able to independently write a scientific paper. They can give presentations on their topic and demonstrate this competence in the preparation of their bachelor's thesis. Data Storytelling Semester: 5 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 1 Exam type: Immanente Beurteilung How the brain processes data and information and deals with complexity; principles of technology adoption; Principles of behavioral decision making; principles of data and technology communication; data storytelling communication tools; realization of a data storytelling project. Scientific Work Semester: 5 Type: Pflicht, Seminar ECTS: 2 Exam type: Immanente Beurteilung Methods of science; research, scientific databases; citation rules; structure of scientific documents; the use of literature management programs (BibTeX and Citavi) and technical word processing software (LaTeX).
Law and Business			7.5
Law and Business Graduates will have a solid understanding of IT law and the specifics of IT law, enabling them to assess its impact on legal transactions, liability, licensing, and usage rights, among other things. Furthermore, they will be familiar with the legal issues arising from the use of AI systems and will be familiar with the principles of the EU AI Act. Graduates will be familiar with the fundamentals of economic relationships and structures. Students will thus have a framework within which they can confidently classify subsequent content, particularly at the interface between information technology and business in the economic dimension. Above all, they will understand the fundamental meaning of economic metrics, particularly in service companies. Graduates will understand the fundamentals of business models and product management and the associated issues. Based on this fundamental knowledge, they will be able to work as AI engineers on the design of products and services for AI products and to align business requirements with technical issues. Basics of Business Semester: 6 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Business administration, basic economics; stakeholders and shareholders, corporate goals and service areas (R&D; procurement, production, sales). Accounting: role of accounting in the company; goals and tasks of double financial accounting, basic systematics, simple posting cases; theses, basicsof accounting; balance sheet analysis; case studies from IT companies;Income-expenditure account (start-up scenario). Digital Business Models and Product Management Semester: 6 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Non-digital and digital business models; Product versus service business; product life cycle management and product roadmaps; minimal viable products; B2C and B2B products; BCG matrix; product marketing and branding; product internationalization; product development and innovation; competitor analysis; requirement engineering and product strategy; KPIs IT and AI Law Semester: 6 Type: Pflicht, Integrierte Lehrveranstaltung ECTS: 2,5 Exam type: Immanente Beurteilung Contract law for IT (on-/offline) including types of contract, consumer protection including information obligations/rights of withdrawal, use of general terms and conditions, special features B2B and B2C, jurisdiction of the courts for IT/EDP law, online/offline advertising and the restrictions,Electronic signature/copyright, licenses/rights of use, patent law, trademark protection law, software (protection)/contracts, payment transactions & new media, telecommunications law, liability issues from contracts/for providers, damages/product iability/warranty/guarantee im EDP/software area, liability issues as an entrepreneur/GM/programmer in the context of the various contract/company forms for the IT industry. Liability issues regarding the use of AI Systems and the AI Act of the European Union.

Projects

	4.	5.	6.
Study Project	10
Study Project Graduates will be able to apply the theoretical knowledge acquired in the previous modules in collaborative projects with real clients, thereby deepening their understanding and, above all, the practical relevance of the knowledge acquired. Study Project Semester: 4 Type: Pflicht, Projekt ECTS: 10 Exam type: Immanente Beurteilung Realization of an extensive AI project with a real client (from industry/business or research) in a team, going through defined project phases, practicing the procedural methods and models as well as documentation. Analysis of the task, selection of ready-made AI components and/or data-based procedure (data selection, pre-processing and exploration; selection of suitable AI methods and, if necessary, training of models), evaluation of the results, implementation and, if necessary, integration.
Internship		25
Internship Graduates can apply the theoretical knowledge they have acquired during their internship at a real company, thereby deepening their understanding and, above all, the practical relevance of the knowledge they have acquired. The internship report primarily serves as documentation of the internship and also promotes writing skills. Internship Semester: 5 Type: Pflicht, Berufspraktikum ECTS: 23 Exam type: Immanente Beurteilung at least 70 full-time attendance days) on an AI project. The students are not only supervised by an employee of the company, but also by a lecturer of the course. The internship is documented as part of the internship seminar (seebelow) in the form of a seminar paper. This is supervised and assessed by the same teacher as the internship. Internship Seminar Semester: 5 Type: Pflicht, Seminar ECTS: 2 Exam type: Mündliche oder Schriftliche Prüfung Accompanying the internship, the internship is documented in the form of a seminar paper. This is supervised by the same teacher as the internship.
Bachelor Thesis			10
Bachelor Thesis Graduates are able to write a scientific paper, research a topic, give a presentation on their topic and demonstrate this competence in the preparation of their bachelor's thesis. Bachelor Examination Semester: 6 Type: Pflicht, Abschlussprüfung ECTS: 1 Exam type: Mündliche oder Schriftliche Prüfung Final Bachelor Examination Bachelor Thesis Semester: 6 Type: Pflicht, Bachelorarbeit ECTS: 8 Exam type: Schriftliche Prüfung The bachelor thesis comprises a theoretical and a practical, project-related part. This work is to be written independently. Methodological and contentrelated support and supervision (procedure, structured writing and professional evaluation and presentation of the results) will be provided by lecturers at the University of Applied Sciences Upper Austria. As part of this course, the substantive and written elaboration of the bachelor thesis takes place. Finding a topic already starts in the 5th semester in the Course “Scientific Work”. Bachelor Thesis Seminar Semester: 6 Type: Pflicht, Seminar ECTS: 1 Exam type: Immanente Beurteilung The bachelor seminar serves to prepare for the preparation of the written bachelor thesis. The students know the requirements for scientific work, in particular the requirements for the written form. They present their work progress for discussion in the bachelor seminar, ccompanying the preparation of their thesis. General conditions for writing the bachelor thesis, joint reflection in the group, clarification of problems and difficulties, clarification of theoretical content, methodological and formal questions. Because the students present their bachelor thesis twice and receive feedback on it, they are already preparing for the bachelor's examination at the end of the sixth semester.

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