Computer Science and Artificial Intelligence (B. Sc.)

This module introduces the fundamentals of software development and programming in Java. Topics include algorithmic thinking, core language elements, control structures, object-oriented concepts, exception handling, arrays, and UML class diagrams. In the lab sessions, students deepen their knowledge through programming exercises using professional tools such as Eclipse, JUnit, and debugging environments.

This module introduces fundamental concepts of computer science. Topics include algorithms and their representations, complexity and computability theory, number and information representation, and logic. Students also explore the structure of modern computer systems, from the von Neumann architecture to advanced concepts such as caching, multi-core systems, pipelining, superscalar processing, and GPU computing.

This module provides the mathematical foundations for computer science and Computational Life Sciences. Topics include propositional and predicate logic, proof techniques, modular arithmetic, complex numbers, limits and continuity. Students also study differential and integral calculus, including Taylor polynomials, Taylor series, and infinite series, with a focus on their fundamental applications.

This module introduces the fundamentals of probability and statistics. Topics include descriptive methods for data analysis and visualization, probability models and distributions, and inferential statistical techniques. Students learn parameter estimation, confidence intervals, and hypothesis testing. Correlation and regression analysis are also covered, providing essential tools for the analysis and interpretation of data.

In this module, students work in teams to develop a chatbot that answers questions about the CAI degree program and studying at TH Ingolstadt. Project results are presented and discussed within the group. Additional workshops on learning strategies, time management, and intercultural competence support a successful start to academic studies. A library introduction provides essential skills for using academic information resources.

This module expands programming skills through key concepts of software development. Topics include algorithms, data types and data structures, control structures, functions, modules, and object-oriented programming with classes and objects. Advanced topics such as exceptions and event-driven programming are also covered. In the lab, students apply and deepen their knowledge using professional development tools.

This module introduces advanced foundations of computer science. Topics include data structures such as arrays, trees, graphs, and hashing, as well as algorithmic approaches including greedy methods and dynamic programming. The course also covers operating systems, processes, memory and file management, and the fundamentals of modern communication networks, protocols, and secure data transmission.

This module provides advanced mathematical foundations for computer science and Computational Life Sciences. Topics include linear algebra with vector spaces, matrices, eigenvalues, and linear transformations, as well as multivariable differential and integral calculus. Students also study functions of several variables, optimization problems, and the fundamentals of ordinary differential equations.

This module introduces fundamental algorithms and methods of artificial intelligence. Topics include logic and fuzzy logic, machine learning, data preparation and preprocessing, as well as supervised and unsupervised learning techniques. Students learn regression and classification methods, gradient-based optimization, validation and evaluation approaches, and modern frameworks, applying them to practical machine learning problems.

This module introduces the fundamentals of scientific research and academic work. Students learn research methods, project management, and techniques for literature review, documentation, and presentation of scientific results. The course also addresses ethical and legal aspects of research. It provides a strong foundation for preparing and completing academic theses, including bachelor's, master's, and doctoral projects.

This module introduces the fundamentals of software engineering across the entire development lifecycle. Topics include requirements analysis with stakeholders, use cases and UML modeling, software architecture and design concepts, as well as implementation principles and code quality. Students also learn software testing methods, including black-box, white-box, and dynamic testing techniques.

This module introduces the foundations of modern web technologies and web development. Topics include HTML, CSS, HTTP, JavaScript, Ajax, and JSON, as well as server-side programming with Python and JavaScript. Students learn about web services, security and privacy aspects, and responsive web design. Through practical exercises, they develop web applications and explore the MVC pattern and the Django framework.

This module introduces fundamental and advanced optimization methods. Topics include the classification of optimization problems, analytical and numerical optimization techniques, gradient-based methods, as well as linear, integer, and binary optimization. Students also study graph-based optimization approaches, including tree structures, shortest-path algorithms, and minimum spanning trees.

This module expands students’ knowledge of machine learning with a focus on neural networks and deep learning. Topics include structured, unstructured, and temporal data, backpropagation, optimization techniques, convolutional and recurrent neural networks, regularization, and hyperparameter tuning. Students also explore unsupervised learning methods such as clustering, autoencoders, and dimensionality reduction techniques.

This module provides an overview of data analytics and data visualization methods across the full data analysis workflow. Topics include data collection, preparation, transformation, and analysis, as well as handling time series, text, and spatial data. Students learn principles of visual communication, interactive visualization techniques, and visualization tools, applying them to real-world datasets through practical exercises using Python.

This module introduces the fundamentals of modern database systems and big data technologies. Topics include relational databases, data modeling, SQL, transaction management, NoSQL concepts, and distributed data storage. Students explore big data architectures, optimized storage formats, distributed file systems, and frameworks such as Hadoop and Spark, as well as modern data management approaches in data lake environments.

This module introduces the fundamentals of natural language and speech processing. Topics include text and audio processing, language models, statistical methods, and neural networks for language and speech understanding. Students explore applications such as text analysis, information retrieval, machine translation, automatic speech recognition, speech synthesis, and conversational systems including dialogue agents and chatbots.

This module introduces methods of image processing and computer vision. Topics include classical image analysis and feature extraction techniques as well as modern deep learning approaches using neural networks. Students learn methods for image classification, object detection, segmentation, and image registration. Through practical exercises, they implement algorithms and train AI models using frameworks such as PyTorch, TensorFlow, and Keras.

This module explores advanced artificial intelligence methods and their practical applications. Topics include machine learning approaches for recommender systems, fraud detection, biometric recognition, and sentiment analysis. Students also study distributed and symbolic AI concepts, including multi-agent systems, swarm intelligence, knowledge representation, logic programming, search algorithms, machine reasoning, and constraint satisfaction techniques.

This seminar deepens students’ academic and research skills through selected topics in computer science and artificial intelligence. Students independently review scientific literature, prepare and deliver a presentation, and discuss their findings with peers. In addition, they write a seminar paper summarizing the topic and its key insights, further developing their abilities in analysis, scientific communication, critical reflection, and academic writing.

The Pre-Internship Seminar prepares students for their upcoming internship experience. The course focuses on reflecting on personal expectations, strengths, and areas for development, as well as addressing uncertainties related to professional environments. Through personality assessments, group exercises, and role-playing activities, students develop communication and conflict resolution skills and strengthen their readiness for a successful transition into the workplace.

The internship enables students to apply their academic knowledge and skills in a professional environment. Students select a suitable host company in Germany or abroad and independently work on defined tasks using scientific methods. They prepare a structured work plan with clearly defined work packages and document their activities, results, and experiences in a comprehensive internship report.

The Post-Internship Seminar supports the reflection and evaluation of experiences gained during the internship. Students present their activities, results, and key insights in short presentations, followed by discussions with their peers. Through professional exchange and immediate feedback, they enhance their presentation, communication, and reflective skills while strengthening the connection between practical experience and academic learning.

This module introduces key concepts of cybersecurity for protecting IT systems and applications. Topics include cryptography, authentication, access control, network security, security principles, and risk management. Students also learn secure software engineering practices, threats and protection mechanisms for AI systems, and the application of artificial intelligence in cybersecurity, including intrusion detection and malware analysis.

This module introduces human-computer interaction and explainable AI within a user-centered design framework. Students learn how to design, prototype, and scientifically evaluate user interfaces. Topics include human factors, interaction technologies, usability methods, and approaches for making AI systems understandable and transparent. Practical exercises cover requirements elicitation, user studies, and the development and evaluation of explainable AI applications.

This module introduces the fundamentals of business administration and entrepreneurship for developing and implementing innovative business ideas. Topics include organizational management, leadership, production, marketing, investment management, and innovation management. Students learn how to generate business ideas, design business models, and evaluate them through practical case studies and business simulations.

This module introduces the methods and tools of traditional and agile project management. Students learn how to plan, manage, and control projects from goal definition through implementation, including resource and risk management. Topics include stakeholder management, effort estimation, project controlling, and agile approaches such as Kanban, Scrum, and hybrid project management. Group exercises reinforce the practical application of these methods.

In this project module, students work as a team on a semester-long, practice-oriented project in the fields of Computer Science and Artificial Intelligence, often in collaboration with companies or research institutes. They independently organize and manage the project, select appropriate project management methods, and develop solutions to real-world challenges. The course strengthens technical, methodological, teamwork, and communication skills in a professional project environment.

This module introduces the ethical and legal foundations for the responsible use of technology and artificial intelligence. Topics include major ethical theories, technology and risk ethics, human–machine interaction, algorithmic bias, machine ethics, and human enhancement. Students also learn legal fundamentals and key regulatory aspects specific to artificial intelligence and its practical applications.

Elective modules allow students to tailor their studies to their individual interests and career goals. The portfolio is updated regularly and covers current topics in computer science and artificial intelligence. Possible focus areas include robotics, autonomous driving and flying, AI in industry and life sciences, mobile and cloud computing, data protection, next-generation networks, quantum computing, and other emerging technology fields.

Elective modules allow students to tailor their studies to their individual interests and career goals. The portfolio is updated regularly and covers current topics in computer science and artificial intelligence. Possible focus areas include robotics, autonomous driving and flying, AI in industry and life sciences, mobile and cloud computing, data protection, next-generation networks, quantum computing, and other emerging technology fields.

The bachelor’s thesis represents the final academic achievement of the degree program. Students independently address a challenging problem in computer science or artificial intelligence using scientific methods. This includes literature research, project planning and execution, selecting and applying appropriate solution approaches, evaluating results, and presenting findings in a clear and scientifically structured written thesis. This seminar supports students throughout the preparation of their bachelor’s thesis. In addition to a general information session covering procedures, requirements, and academic standards, students participate in regular meetings with their supervisors. These sessions focus on discussing progress, reflecting on methodological approaches, and addressing scientific questions to support the successful completion of the thesis.