AI for Sustainable Technologies
Agile Project Management
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1APMIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Agile management and control of projects and processes; Process models in software development (see Scrum, KANBAN); Agile basic principles and values ¿¿(value driven delivery, minimal viable product, self organizing teams, simple & focused communication); Process models for decision-making; Digitalization & Agility Interdependencies; Stages of development towards an agile organization; Getting to know different templates, tools and methods that support an agile approach in projects. Differentiation from classic project management. Verification of the strengths and weaknesses of the tools used depending on the IT area of ¿¿application
Learning Outcomes:
Based on knowledge of the goals and theoretical foundations of agile project management, graduates implement their own projects using the appropriate tools and methods and critically reflect on how agile approaches affect implementation.
Superior module:
xxx
Module description:
xxx
Analytics & Knowledge Discovery
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1AKDIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
The Machine Learning Workflow, data import, data coding, exploratory data analysis, data cleaning, handling of missing values, feature generation, Curse of Dimensionality, Kernel Density Estimators, multivariate normal distribution, Gaussian Mixture Models, PCA, t-SNE, K-means, Hierarchical Clustering. Spectral Clustering, Distances and Similarity Measures
Learning Outcomes:
The course focuses on unsupervised learning and the first part of the machine learning workflow. Graduates import, code and visualize different types (numerical, categorical, text) of data as part of the machine learning workflow. They recognize errors in data and deal with them accordingly. They also deal with missing values in an appropriate manner so as not to negatively influence subsequent analyses. If necessary, they reduce the dimensionality of the data (e.g. with PCA or tSNE) for visualization, and visualize densities with kernel density estimators in addition to histograms. They identify clusters using the standard methods Gaussian Mixtures, kmeans, hierarchical clustering. Outlook: more flexible methods such as spectral clustering.
Superior module:
Data Science & Analytics
Module description:
xxx
Data Science & Artificial Intelligence
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1DSAIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Lecture content:
Definition of Terminology in Data Science and Artificial Intelligence, Design Cycle, Extended Design Cycle, Sampling, Pre-processing, Normalization, Performance Measures, Cross Validation, Training Policies, K-nearest Neighbour and Minimum Distance Classifier, NLP Pre-processing and Features, Low Level Image Features
Learning Outcomes:
After completing this course, graduates will be familiar with the types and components of data science projects and be able to describe their structure. They will be able to name the roles and tasks of project team members. They will understand the concepts of data, models and algorithms and their relationship. They discuss the suitability of a data collection or a planned data data collection process with regard to a data science project. Graduates use the classical approach to extract information from data in various forms of representation (numerical, categorical, one-hot or text). They collect, process and visualize data to gain a basic understanding of it. They follow the supervised learning design cycle by implementing data-specific feature generation, sampling training and test data, training selected classifiers and analyzing their performance. Graduates use state-of-the-art development tools and scalable technologies and argue their approaches professionally.
Superior module:
Data Science & Analytics
Module description:
xxx
Discussion & Argumentation Skills
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1DASIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Argument, negotiation and discussion techniques, use of appropriate phrases and rhetorical devices, practical examples and role plays
Learning Outcomes:
Graduates can present a topic clearly and understandably in English. They are able to build arguments logically and rigorously and to respond to questions and counter-arguments to be linguistically competent.
Superior module:
xxx
Module description:
xxx
Distributed Systems & Cloud Technologies
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1VSCIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 3 |
| ECTS Credits | 4 |
| Examination character | immanent |
Lecture content:
Application-related communication paradigms; cross-platform protocols and services as well as distributed data management; overview of component technologies; development, integration and deployment paradigms for distributed software systems; cloud service models; communication technologies for time-dependent data streams; current topics and application examples of software technologies.
Learning Outcomes:
of thinking, consciousness and emotions and analyze the associated ethical and moral aspects. They should also understand the concept of machines as moral actors and get to know different approaches to implementing morality in machines. By analyzing ethical issues in the context of interaction between
Superior module:
Informatics
Module description:
xxx
IT- & Security Management
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1ITMIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
IT Management & Enterprise Architecture; Creation and use of information technology, planning and control of IT (= IT strategies), interaction between IT and specialist departments (e.g. marketing, controlling, finance); TCO analysis; relevant standards and frameworks such as ISO17799 and ISO20000, change management, problem management (helpdesk), security management, life cycle management, disaster recovery measures; Hybrid Distributed Cloud & Cloud Operations; Automation in Software Development (DevSecOps). IT security organization, governance, risk compliance; Classification of IT security, national and international information security standards and frameworks (e.g. ISO27000, IT Grundschutz), cyber security strategies, security life cycle, security policies / standards / guidelines / procedures, ethical hacking and penetration testing, IT and malware forensics , Incident Handling and Computer Emergency Response Team (CERT), legal bases and legal peculiarities from the Telecommunications Act and data protection law, among others.
Learning Outcomes:
The graduates have the necessary knowledge to successfully design and manage IT in a company. They see themselves as future IT & security managers the relevant operational, legal and social environment and master the structure Management (roles/access rights) of an IT infrastructure in order to comply with the EU Data Protection Regulation, among other things. You have the ability to align IT with the company organization and needs and understand IT as part of operational processes. In addition, they can manage IT as a business and enable core business processes to be improved through innovative technologies (Technology Business Management / CTO). You can assess security threats and know current countermeasures. The graduates can also implement technical measures for IT security independently and competently.
Superior module:
xxx
Module description:
xxx
Mathematics and Modelling
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1MAMIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 4 |
| ECTS Credits | 5 |
| Examination character | immanent |
Lecture content:
Vector valued functions on n-dimensional domains, vector fields, scalar fields, partial derivatives, gradient operator, Jacobi and Hessian matrix, directional derivative, Taylor series in several variables, critical points, local minima, maxima and saddle points, convex optimization and applications. Integral calculus, Pre-Hilbert (inner-product) space, (orthonormal-) basis and basis transformation, Eigenvalues, Eigenvectors, matrix decompositions and applications (PCA).
Learning Outcomes:
Alumni can apply functions in several variables to model problems. They are able to analyze the change behavior of these functions and to determine critical points. They can approximate complex functions by multidimensional polynomials (especially with tangent planes and second order Taylor polynomials). They are able to use gradient based methods to find local minima. They understand selected problems of convex optimization and can solve them with mathematical software. Alumni are able to calculate the most important matrix decompositions and apply eigenvalue theory to perform the principal components analysis for data. Alumni can solve multidimensional integrals. They understand the notion of a vector space (VS) with inner product and relate to it in different application areas. They master the coordinate transformation for the change of basis in finite dimensional VSs and are familiar with the relationship to Fourier analysis. They know selected application areas of the mentioned methods.
Superior module:
xxx
Module description:
xxx
Software & Process Notations
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1SPNIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Textual and graphical notations for software development and process modelling (e.g. BPMN, SPEM); notations for service and interface specifications; use of common notation tools; use of domain-specific UML profiles; meta-modelling; current topics in software notations.
Learning Outcomes:
People and Machines, graduates should be able to identify moral challenges in connection with current developments in autonomous, intelligent machine technology and examine case studies such as autonomous weapon systems, care robots and autonomous driving in order to derive ethical perspectives. In addition, they should be able to analyze complex ethical questions in the field of machine ethics, evaluate different viewpoints, make well-founded judgments, construct ethical arguments and communicate their opinions convincingly both orally and in writing. In addition, graduates should develop an awareness of ethical considerations in connection with the use of machines and be able to reflect and question their own ethical values.
Superior module:
Informatics
Module description:
xxx
Target Group-oriented Communication
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | AITM1ZTNIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Socio-ecological framework along the Sustainable Development Goals (SDGs); managing complexity and solutionism in the environment of green technology; addressing the society-technology-impact: how society affects technology development and how the development of technology affects societies; understanding the narrative of AI in relation to sustainable development and contributing to culture; experiencing political level and governance aspects of sustainable development and green technology and the concept of global citizen education; testing individual-ethical action of human-centred technology development; integrating the concept of heteronomy into the future professional role.
Learning Outcomes:
Graduates understand the mutual influence of technology and society; they are able to reflect on the impact of society and technology development; they can develop a human-centred perspective of their technology; they reflect on the aspects of sustainable development on technology and society; They can understand their professional role as a technological developer in society and actively use their role in changes to the (professional) environment and help to shape these in the interests of sustainable development; they can name lifeworld influencing factors (of the working and living environment) and understand them as a significant part of a human-centred technology. Graduates are able to theoretically categorise the techno-sociological perspective and know which ethical standards are currently being discussed and implemented at EU level.
Superior module:
Ethics & Sustainability 1
Module description:
xxx
Applied Statistics
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2ASTIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 4 |
| Examination character | immanent |
Lecture content:
Estimation theory: Point and interval estimators, maximum likelihood method, method of moments, parametric and non-parametric models (kernel density estimators, normal distributions, mixed models), statistical tests, study design and analysis of variance. Data visualization. Outlook: Random numbers and randomization; Graphical models and applications.
Learning Outcomes:
Alumni can apply methods of inferential statistics to data and communicate the results obtained both verbally and graphically. They can describe data with models and are able to represent dependencies of random variables with graphical models. They know statistical standards and are able to plan, conduct and document experiments. They know applications of random number generators in the area of generative models and can produce corresponding data with mathematical software.
Superior module:
xxx
Module description:
xxx
Machine Ethics
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2METIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 1.5 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Theoretical foundations: artificial intelligence, thinking, consciousness and emotions, ethics and morals; Machine ethics: machines as moral actors, moral implementation, humans and machines; Scope and examples
Learning Outcomes:
Graduates should have a basic understanding of the concepts of artificial intelligence, of thinking, consciousness and emotions and analyze the associated ethical and moral aspects. They should also understand the concept of machines as moral actors and get to know different approaches to implementing morality in machines. By analyzing ethical issues in the context of interaction between People and Machines, graduates should be able to identify moral challenges in connection with current developments in autonomous, intelligent machine technology and examine case studies such as autonomous weapon systems, care robots and autonomous driving in order to derive ethical perspectives. In addition, they should be able to analyze complex ethical questions in the field of machine ethics, evaluate different viewpoints, make well-founded judgments, construct ethical arguments and communicate their opinions convincingly both orally and in writing. In addition, graduates should develop an awareness of ethical considerations in connection with the use of machines and be able to reflect and question their own ethical values.
Superior module:
Ethics & Sustainability 1
Module description:
xxx
Machine Learning
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2MLGIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Lecture content:
Recapitulation of Learning Theory, Representation Learning and Self Supervised Learning with Autoencoders, Classification and Regression Trees with Bagging and Boosting, Probabilistic Models and Bayes Classification, Minimum Risk Classification, Introduction to Deep Learning with Convolutional Neural Networks, Selected Topics from Machine Learning
Learning Outcomes:
Graduates understand the assumptions and limitations that a particular model choice entails with regard to the theory of statistical learning and the ¿no free lunch¿ theorem. They select common machine learning algorithms, parameterize them and evaluate the effects of different design decisions. Graduates recognize overfitting and underfitting during the training process and take appropriate countermeasures such as regularization. They apply the machine learning models to different data types for tasks such as classification, representation learning and object recognition and discuss possible effects in concrete use cases in terms of sustainable technology use
Superior module:
Data Science & Analytics
Module description:
xxx
Modern Software Architectures
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2MSAIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Fundamentals and characterisation of software architectures; architecture-related quality attributes; model and service-oriented architectures; software architecture development and system integration; strategies and techniques for integrating heterogeneous systems; reference architectures and enterprise integration patterns; software architecture evaluation and architecture metrics; architecture documentation; current topics on software architectures
Learning Outcomes:
Graduates evaluate contemporary software architectures and can make sound arguments for architecture decisions for development and integration projects. They apply software design patterns and architecture patterns (in particular enterprise integration patterns) and can make IT abstraction methods comprehensible and usable for the stakeholders involved. They recognise innovation-relevant issues and independently develop suitable solution concepts in order to systematically manage a high degree of technical and methodological heterogeneity.
Superior module:
Informatics
Module description:
xxx
Project 1
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2PRJPT |
| Type | PT |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 4 |
| Examination character | immanent |
Lecture content:
Research and development-oriented project work with technical and methodical elaboration of topics from the courses. Identification of topics together with internal and external stakeholders with close reference to the Sustainable Development Goals in an initial idea generation phase by means of a design jam. Focus on understanding the business model and the domain and data as well as method selection, modeling and provision or transfer to live operation in accordance with the CRISP-DM model. Accompanying project management, reflection and coaching on teamwork as well as preparation and target group-oriented communication of the project results.
Learning Outcomes:
The graduates possess implementation skills related to the teaching content in the areas of Machine Learning and Analytics, Cloud Technologies, as well as Software Engineering and Architectures. By developing sustainable solutions for their project goals, they deepen their subject-matter and methodological expertise in selected application fields such as Language Technologies, Industrial Reinforcement Learning, or Image Processing. In the 2nd term, the teams focus on the areas of business and data understanding as well as the systematic selection of suitable methods for solving the problem. This selection is also discussed in relation to societal conditions and the goals of sustainable technology development. This phase is characterized by a spirit of experimentation, problem-solving ability, willingness to learn, and holistic thinking on the part of the graduates. During the contact hours, the graduates are supported by coaches from the aforementioned technical areas and topics. They are also supported throughout the entire project duration by a subject supervisor and in agile project management. With their support, the graduates succeed in refining their solutions, working in a structured manner, and presenting in a way that is appropriate to their target audience. They are capable of engaging in discussion and justifying their decisions.
Superior module:
xxx
Module description:
xxx
Sales, Marketing & Digital Innovation
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2VMDIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Content from sales and marketing is the understanding of market research methods and their areas of application, marketing mix, product policy, brand policy, pricing policy, sales policy, sales management, key account management, analysis of best practice examples. The main goal of Digital Innovation is to understand how digitalization in innovation management influences the development of new business models as well as the process of innovation development and management. Approaches such as design thinking or design innovation are discussed here. The aim of the course, which is to be carried out in an integrative manner, is to inherently network the two subject areas. In the interaction between sales, marketing and digital innovation, the focus is on the integration of digital technologies into sales and marketing strategies. Conversely, it is inherent that in the context of a creative Marketing activities supporting the innovation process are generated. As part of the course, this interconnection is ensured through practical exercises and compact projects.
Learning Outcomes:
The graduates know the essential basic concepts of ¿sales and marketing¿ and their practical significance. The graduates work on a complex task from different areas of business (case studies) and solve a problem independently and document these in an engineering manner. The graduates have an overview of the subject areas of digital innovation and digital transformation. The graduates have knowledge of how the digital economy works (Industry 4.0, sharing economy, platform economy) and have a basic understanding of the importance of digital transformation for business processes and models
Superior module:
xxx
Module description:
xxx
Software Engineering & Operations
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2SEOIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 3 |
| ECTS Credits | 4 |
| Examination character | immanent |
Lecture content:
Software and systems engineering; process models; requirements engineering; software and system architectures; function development; design-for-X; software quality and quality assurance, clean code; software engineering techniques for software development on a large scale; current topics in software engineering.
Learning Outcomes:
Graduates understand the various tasks and activities within the software and system development process (requirements engineering and software quality; software architecture; detailed design and design-for-X; verification and validation) and systematically master the challenges of organising various business-relevant development projects in the field of cyber-physical systems. Graduates assess process models and develop them further independently and independently drive the conception, implementation and monitoring of professional software and system projects.
Superior module:
Informatics
Module description:
xxx
Target Group-oriented Communication
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2ZOKIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 1.5 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Identifying contact persons and selecting methods to reach the target group. Forms and framework conditions of effective feedback, exercises and role plays. Individual processing of the input presented and developed in the course, supported by selective coaching
Learning Outcomes:
The graduates are able to present complex content in a target group-oriented manner to develop clearly structured lines of argument. You can be solution and benefit oriented argue and formulate criticism objectively and constructively. You are able to accept criticism and give feedback accordingly.
Superior module:
xxx
Module description:
xxx
Time Series Prediction & Business Forecasting
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | AITM2TSPIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Type of Variables and Data, Data Pre-processing, Decomposition of Time Series, Statistical Models for Time Series (SARIMAX), Setup for Training, Validation and Testing, Machine Learning approaches for Time Series Prediction and Classification, Outlier Detection, Clustering of Time Series, Assessment of precision of forecasts by means of confidence intervals, Applications to Demand Prediction.
Learning Outcomes:
Graduates identify exogenous and endogenous variables and prepare time series data for further processing. They decompose time series in order to analyze trends and seasonal components. They adapt statistical models to suitably normalized data and use the model to predict future values or to classify the current state. They discuss the appropriateness of machine learning approaches compared to statistical modeling in terms of data efficiency. They implement the machine learning design cycle and evaluate prediction accuracy using customized cross-validation. They identify outliers in time series and use this knowledge to develop more robust models. They cluster time series to summarize and visualize typical shapes and progressions. They use the methods they have learned to implement aspects of example scenarios such as a demand forecasting system or automated warehousing. They identify sustainability risks arising from the use of the technology and the data used and discuss appropriate countermeasures.
Superior module:
Data Science & Analytics
Module description:
xxx
Applied AI Lab
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3AALLB |
| Type | LB |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Advanced Methods in either the area of Language Technologies, Image Processing or Reinforcement Learning are reviewed, and selected applications are chosen and implemented by employing corresponding tools. Introduction into Machine Learning Operations (MLOps) and application of concepts regarding version control and collaboration, experiment tracking, model deployment, monitoring and logging onto AI solutions
Learning Outcomes:
Graduates choose one of the areas: language technologies, image processing or reinforcement learning. They discuss application scenarios and select a setup for closer examination. They acquire advanced methodological and implementation skills for the selected scenario and become familiar with the relevant tools. They use these to implement an AI-based solution. They discuss and defend their approach with regard to the goals of sustainability and possible social impacts. In addition, graduates build an MLOps pipeline to run through the complete life cycle of an AI application.
Superior module:
Applied AI
Module description:
xxx
Business Management & Founding
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3UFGIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
The aim of this course is to understand how traditional corporate management and digital leadership work and their effects on companies and managers; A particular focus is on modern management approaches such as digital leadership in practice, agile leadership; Leading virtual teams; Digitalization & Agility; Dealing with virtual communication, eLearning tools in the company. The focus regarding company formations (company formations) deals with start-up management, developing a business idea, business case & product innovation (through business Canvas & Value Proposition Canvas); types of financing; Development of a business plan, implementation of innovative simulation games (Apollo 13 or Target SIM). Theoretical content is deepened with technology-focused case studies from business.
Learning Outcomes:
The graduates have an overview of the subject areas of corporate management and Starting a business. You know the structure, connections and processes within a company. You know the management cycle and are able to use the most important corporate management tools. You can have a business plan create, as well as the different models of increasingly digital leadership and Use relevant procedures, strengths and weaknesses and differences in a differentiated manner and assess the impact on corporate culture.
Superior module:
xxx
Module description:
xxx
Deep Learning for Image Analysis
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3DLGIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Deep learning architectures for the analysis of image data, object detection, semantic segmentation and instance segmentation, image translation, architectures for the analysis of 3D image data, self-supervised and representation learning architectures, data augmentation; parameterization, model selection and design. Tools: Python, Pytorch/Tensor-Flow, Anaconda, Git, Unix/Bash, GPUs. Further aspects: Optimal use of hardware (GPUs, GPU clusters) and software resources.
Learning Outcomes:
Graduates are familiar with both basic and current approaches and methods from the fields of deep learning and representation learning for image analysis and are able to apply these to data sets using suitable toolboxes. In practical tasks, they find a suitable model structure and according model parameters and decide on the use of pre-trained models in terms of transfer learning. They are familiar with methods of semi-supervised learning and data augmentation to optimize the effectiveness of small data sets using domain knowledge (Small Data Challenge). They parameterize the respective learning algorithms and apply them to data sets with optimal use of hardware and software resources. They are able to develop innovative applications with these methods and know the limits and areas of application of the respective algorithms. Graduates of the Master's in Industrial Informatics & Robotics are given an overview of neural networks at the beginning. Graduates of the AI for Sustainable Technologies Master's program will receive an in-depth look at specific content.
Superior module:
Applied AI
Module description:
xxx
Ethics & Sustainability
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3ETNIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
The need for (professional) ethical orientation has never been greater than in the last decade. We currently encounter ethics in a wide variety of forms and in a wide variety of ways Hyphenated variants: bioethics, medical ethics, animal ethics, ethics and politics, ethics and economics, ethics lessons instead of religious lessons in schools, from addressee to environmental ethics, from everyday life to systems ethics. Our existence seems to be moving in ethically and morally charged times, especially because the terms ethics and sustainability themselves are used in an increasingly blurred and inflationary manner. The symposium therefore attempts to make a contribution to clearing up the jungle of concepts and to raise awareness of (professional) ethical questions and questions about sustainability.
Learning Outcomes:
After completing the symposium, graduates are able to deal with ethical and moral dilemmas to analyze and reflect; Opinions from a lecture in your own context of action to evaluate; social issues with a view to their own technical/professional environment argue; to articulate and justify their own opinions in the group discussion; After the general, university-wide part, the results are processed in relation to the topics of the degree program in a self-study phase with subsequent feedback.
Superior module:
Ethics & Sustainability 2
Module description:
xxx
Intercultural Communication Skills
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3ICSIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1.5 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Basics of perception psychology that are important for intercultural communication, definition of intercultural interaction and communication skills, interaction pitfalls, practical application through interaction games and exercises
Learning Outcomes:
Graduates can identify the complex factors that influence communication in intercultural contexts. They are able to classify their own culturally determined role in the context of communication.
Superior module:
xxx
Module description:
xxx
Language Technologies & Applications
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3LTAIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Applications in the field of language technologies that can be implemented using natural language processing methods, e.g. text generation, machine translation, text classification, social media analysis, information retrieval, conversational AI. Methods: Language models (recurrent and transformer-based), contextualized representations, attention-based models, tokenization. Tools: Python, scikit-learn, nltk, tensorflow/keras, huggingface.
Learning Outcomes:
After completing this course, graduates will be able to independently acquire the latest knowledge in the field of Natural Language Processing based on the knowledge they have acquired. They can analyze facts and develop well-engineered solutions for problems. They are able to design model and parameters depending on the task at hand and know the limits and application areas of the respective algorithms. The flexible time allocation for part of the performance assessment gives graduates scope for independent action and self-management. Group work in the laboratory setting also promotes graduates' problem-solving and cooperation skills.
Superior module:
Applied AI
Module description:
xxx
Master Seminar & Master Exposé
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3MMESE |
| Type | SE |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 5 |
| Examination character | immanent |
Lecture content:
Quality aspects and standards of scientific writing as well as characteristics of a scientific approach to work; importance of theoretical frames of reference; work phases and estimation of workload for the Master's thesis; types and characteristics of scientific publications; methods and tools for literature research and source management; citations and references; methods and tools for writing scientific texts, illustrations, documents and presentations. Use and layout of formulas, tables and graphics; dealing with AI tools and the topic of plagiarism; special application to the systematic structure of an exposé and its discursive defense
Learning Outcomes:
Graduates independently develop the structure and content of academic theses in a goal-oriented manner and design coordinated methodological and practical/empirical sections. They find relevant publications on the subject area of the Master's thesis and develop scientific lines of argumentation. With knowledge of the publication life cycle and the peer review process, they use and evaluate formal, structural and content-related quality aspects of sources. They understand the importance of a scientific-methodical approach and argue their ideas accordingly. They design reproducible experiments and discuss suitable metrics for answering research questions. Graduates write down all required content-related components of the exposé and independently coordinate with the supervisor and ultimately obtain approval from them. A prescribed schedule with work steps and milestones is available, whereby the degree of complexity of the topics and questions of the Master's thesis is appropriate to the time and material resources.
Superior module:
xxx
Module description:
xxx
Project 2
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3PRJPT |
| Type | PT |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 4 |
| Examination character | immanent |
Lecture content:
Research and development-oriented project work with technical and methodical processing of the topics from the courses of the study program. Identification of topics together with internal and external stakeholders with close reference to the Sustainable Development Goals in a preceding idea generation phase by means of a design jam. Focus on understanding the business model and the domain and data as well as method selection, modeling and provision or transfer to live operation in accordance with the CRISP-DM model. Accompanying project management, reflection and coaching on teamwork as well as preparation and target group-oriented communication of the project results.
Learning Outcomes:
Graduates have implementation skills in the areas of machine learning and analytics, cloud technologies, software engineering and architectures. By developing their own tasks and sustainable problem solutions, they deepen their technical and methodological skills in selected fields of application such as language technologies, industrial reinforcement learning and image processing. In the third semester, the teams focus on refining their solution to the problem by optimizing the software architecture and modeling based on previously gained knowledge. The provision of the result as a usable prototype using current ML-Ops approaches forms the finalization of the project and is the basis for reflection in terms of sustainability and future viability of the solutions. This phase is characterized by the graduates' creative drive, decision-making ability and results-oriented action. Coaches from the aforementioned technical fields and subject areas are available to the graduates during the classroom based periods. They are also supported throughout the project by a specialist supervisor and in agile project management. With their support, graduates are able to refine their solutions, work in a structured manner and present them in a target group-oriented manner. They are able to engage in discussions and justify their decisions
Superior module:
xxx
Module description:
xxx
Reinforcement Learning for Intelligent Agents
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3RLGIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Definition von Reinforcement Learning, Mathematische Grundlagen der Markovschen Entscheidungsprozesse, Komponenten des RL (Agent, Policy, Model), Model- und Nicht-model basiertes RL, Optimierung von Policy und Value-Function, Value-Function Approximation, Reinforcement Learning Algorithmen, Proximale Policy-Optimierung. Werkzeuge: Python, scikit-learn, tensorflow/keras
Learning Outcomes:
After completing this course, graduates will be able to independently acquire the latest findings in the field of reinforcement learning based on the knowledge they have acquired. They will be able to analyze problems and develop advanced solutions. They are able to identify problems in reinforcement learning and differentiate them from supervised learning. Depending on the problem, graduates are familiar with applicable methods. The flexible time allocation for part of the performance assessment gives graduates room for independent action and self-management. Group work in a laboratory setting also promotes graduates' problem-solving and cooperation skills.
Superior module:
Applied AI
Module description:
xxx
EC: Big Data Engineering
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3BDEIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Paradigms and characteristics of big data and cloud computing; overview of common big data frameworks and business-relevant cloud infrastructures; programming techniques for data-intensive applications and use of hybrid cloud-based infrastructures for data-intensive software development; implementation of case studies; selected chapters from big data computing.
Learning Outcomes:
Graduates master the technical and organizational challenges of big data processing and apply methods and techniques of data-intensive software development. They use common big data frameworks and utilize the transdisciplinary aspects of cloud computing and communicate its technological foundations. In addition, they implement selected case studies of data-intensive business applications.
Superior module:
xxx
Module description:
xxx
EC: Industrial Image Processing
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | AITM3IBVIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Basic elements of the image processing chain, hardware components (optics, cameras, illumination), basics of image processing (filtering, image enhancement, image segmentation), morphological operations, image analysis in the time & frequency domain, industrial inspection, visual quality control, basics of image learning
Learning Outcomes:
The alumni know the essential hardware components of an industrial image processing system and are aware of their characteristics and possible applications. They master the theory of the most important methods and algorithms and can implement them using common software libraries. You are able to analyze to evaluate image processing tasks in order to develop solutions for industrial image processing. image processing. You will know simple concepts of machine learning and their their applicability in image processing.
Superior module:
xxx
Module description:
xxx
Advanced Presentation Skills
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AITM4APSIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Summarizing relevant specialist texts and scientific articles and preparing them for verbal presentation, target group-oriented and emotionally appealing presentation techniques, use of rhetorical devices, storyboarding and storytelling.
Learning Outcomes:
The graduates can present a topic in English clearly and understandably Use rhetorical devices and elements of storytelling appropriate to the target group. She are able to use the technique of storyboarding in the preparation of a presentation
Superior module:
xxx
Module description:
xxx
Dashboarding & Business Intelligence
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AITM4DBIIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 1 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Basics of dashboarding and business intelligence (BI), data-driven decision-making processes; design and development process of dashboards; design principles; best practices and examples of BI tools; selection of key figures; user-friendliness of dashboards; dashboard categories; visual elements
Learning Outcomes:
Graduates use dashboards and interactive visualizations to support decision-making processes. They develop dashboards based on design principles and best practices from business intelligence theory. They select metrics, KPIs and data sources and display them in a dashboard. They choose suitable visualization components for the given application context.
Superior module:
Applied AI
Module description:
xxx
Explainable AI
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AITM4EAIIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Lecture content:
Introduction to Explainable AI, Local & Global Interpretability, Model-Agnostic Methods (Partial Dependency, Feature Importance, LIME, SHAP), Trustworthy AI (Bias and Fairness in AI, Adversarial Attacks & Defences, Privacy)
Learning Outcomes:
Graduates understand the importance of explainable AI and the consequences of using inexplicable AI systems (black box models). They are familiar with various techniques for achieving interpretability in AI models, such as analyzing the meaning of features, partial dependency diagrams, LIME, SHAP and other model-agnostic methods. They are able to understand the contribution of individual features and model decisions. They have a profound understanding of the challenges of bias and fairness in AI and of techniques for detecting and mitigating bias in models, such as fairness metrics, data augmentation and adversarial training. They also gain knowledge of the key concepts of trustworthy AI, such as privacy, data governance, transparency and accountability. Graduates gain practical experience by implementing explainable AI techniques through practice and project work, where they apply and evaluate different techniques on data sets.
Superior module:
Applied AI
Module description:
xxx
Master Exam
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AITM4MPRDP |
| Type | DP |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 0 |
| ECTS Credits | 2 |
| Examination character | final |
Lecture content:
Presentation and defence of the Master's thesis (English); subject examination discussions
Learning Outcomes:
The graduates coherently and concisely present the motives, the methods used and the results achieved in their Master's theses and provide a well-informed outlook for the future. They answer the questions asked about their Master's thesis in a way that is appropriate to the target audience, explain complex relationships and visualize them adequately. In addition, they establish easily comprehensible cross-connections to key related subjects of the degree program and communicate the innovative aspects of their Master's theses in a generally understandable form.
Superior module:
xxx
Module description:
xxx
Master Thesis
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AITM4MAAIT |
| Type | IT |
| Kind | Diploma/master thesis |
| Language of instruction | German |
| SWS | 0 |
| ECTS Credits | 19 |
| Examination character | immanent |
Lecture content:
Developing and independently working on a problem from the subject areas of the Master's programme at a scientific level with special consideration of the innovative potential of the solutions sought and in compliance with a scientifically oriented approach based on the current state of the literature.
Learning Outcomes:
Graduates write their Master's thesis independently and take a scientific and systematic approach. They analyze and present problems and identify corresponding research questions and objectives, formulate hypotheses and implement the necessary steps. They develop the content of the Master's thesis in line with the teaching and research in the degree program, whereby the graduates argue and justify their approach scientifically and critically question their results
Superior module:
xxx
Module description:
xxx
Reading Group
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | AITM4RGPIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 2 |
| Examination character | immanent |
Lecture content:
Current topics and presentations from the research focus areas of the Department of Information Technologies and Digitalization, specialist presentations from the corporate environment of the three Master's degree programs Cyber Security, Industrial Informatics & Robotics and AI for Sustainable Technology, Cross-course discussion of business-related professional and technical challenges as well as current research results
Learning Outcomes:
Graduates can follow expert talks from the business environment of their own degree program as well as those of other information technology-focused degree programs and discuss the content of these presentations and their conclusions. They are also able to grasp current research results from thematically related research areas, discuss them with graduates from the above-mentioned degree programs and reflect on them critically.
Superior module:
xxx
Module description:
xxx