![[Translate to English:] Frau vor PC, Code/technische Daten in Animation](/fileadmin/_processed_/image-enhancer/50547/ComputerScience_AI_iStock-1244479010-1896x888-crop-default-1-30-1896-1067.webp "[Translate to English:] istockphoto/metamorworks")
Would you like to develop software and understand how artificial intelligence works? Then the Bachelor’s degree in Computer Science and Artificial Intelligence is exactly the right course for you. In this English-taught programme, you’ll gain a solid foundation in computer science: programming, software engineering, web technologies, database systems, big data and data visualisation will form part of your core curriculum. In addition, you’ll learn about key AI methods – including machine learning, neural networks, natural language processing, heuristic optimisation, computer vision and intelligent agents.
Ethical issues, data protection and the security of digital systems also play an important role. You’ll adopt a scientific approach to your work, develop teamwork skills and learn to present your findings clearly. This broad skill set opens up a wide range of career prospects in software and AI development, data science, IT consultancy or international tech companies – wherever digital solutions are shaping the future.
Graduates with a degree in computer science or AI are in demand wherever information is stored, processed and transmitted – which, nowadays, is across all sectors, companies and organisations.
Career prospects in the job market are correspondingly good. All-round IT professionals are just as sought-after as AI specialists.
You will be prepared, above all, for specialist and managerial roles in the following areas: software development, AI development, data science applications, cloud development, IT project management, IT consultancy and training.
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
| 1. Semester | |
|---|---|
| 2. Semester | |
| 3. Semester | |
| 4. Semester | |
| 5. Semester | |
| 6. Semester | |
| 7. Semester |
Based on the regulations for international orientation and selection procedures, the selection of applicants focuses (in addition to your final grade) particularly on your grades in mathematics and computer science (programming-related) courses. A good score on the SAT also increases your chances of admission.
All necessary information for applying to the CSAI program can be found here: https://www.thi.de/en/studies/application/bachelorapplication-from-abroad.
Students can only start their studies in the winter semester. The application period begins on May 2 and ends on July 15, every year.
We are not a distance learning university. There are no more online classes.
Formal eligibility is determined by uni-assist, not by THI. Here you can find out about your prospective eligibility: https://www.uni-assist.de/en/tools/check-university-admission/. The available study places are then assigned in the order of the grades determined by uni-assist. There is no further aptitude test at THI. Prospective students from China, India, Vietnam or Mongolia please note that before submitting your certificates to uni-assist, you should have your documents certified by the Academic Verification Office of your home country.
Formal proof of proficiency in English is not required, nor is proficiency in German. However, it is strongly recommended that you have both a reasonable level of English and a basic knowledge of German.
Cooperative (dual) study will no longer be supported in this program from the upcoming winter semester.
If you miss entire semesters due to visa delays, you can apply for an extension of exam deadlines. However, this application can only be made in person, after you have arrived at THI. To avoid disadvantages, you should definitely arrive at the beginning of the third semester. You can make up the courses of the missed semesters.
If you have already acquired credit points in a previous study program, a credit transfer is possible. However, it can only be applied for and decided upon after enrollment.
On-campus accommodation (e.g. in dorms) is not usual in Germany. You must look for accommodation on the open housing market on your own responsibility. This applies also for student houses.
There are currently no tuition fees at Bavarian public universities. You only have to pay a semester fee of 67 EUR to the Student Services/Studentenwerk. In return, however, there are discounts, e.g. in the refectory.
As of winter semester 2024/25, THI charges service fees of 500 Euros per semester for students from third countries. If you have any questions, please contact incomings@thi.de
Study places are limited. They are assigned according to the uni-assist grades of applicants with priority given to EU applicants, although their number is small. Exact grades or thresholds cannot be given in advance, as they dynamically result from the applicant situation.
If you arrive late and can prove that you applied for a visa in time, you may request for an extension of the exam deadlines by the amount of time missed. You can only make this request if you are there in person. You can take missed modules (and exams) one year later together with students of the following year. Your duration of study will of course be extended as a result.
If you fail to arrive at THI in person by the end of your third semester, all exams from the first two semesters will be considered failed for the first time.
Info for students who arrive in 3rd semester:
THI regulations state that you need to take every exam for the first time by the end of the 3rd semester, otherwise the exam will count as “failed” for the 1st time (failing an exam 3 times will lead to exmatriculation).
The application for withdrawal and grace period can be found in the PRIMUSS portal under Applications and Messages > Select application/form > Others > General Application.
The application form is empty, i.e. students can explain in detail why they need an extension and the reasons for it. The applications and the corresponding supporting documents (confirmation of receipt/application letter from the visa office/embassy and presentation of the visa in the travel application - copy of identity card with arrival stamp in Germany) should be submitted immediately after entry, ideally together with the change of address (change of address from abroad to German address).
Assuming you have access to Moodle (our Learning Management System), you can sign up for the courses remotely. As they progress in the lecture, lecturers upload materials (Documents etc.) about their respective lecture in the moodle course (some moodle course rooms might even contain all the materials for the whole semester). Thus, you can start studying „from home“ and prepare for the time of your arrival in Ingolstadt.
We also recommend learning German as soon as possible in order to be better prepared for daily life in Germany as well as possible entry into a job market (also as interns during your studies).
You may enroll in a higher semester if your previous degree program is considered equivalent to ours and only if there are free study places due to dropouts. Otherwise, you must start in the first semester, but you can have equivalent modules credited.
An application for credit transfer can only be submitted by enrolled students.
Do you have any questions about applications, admission, enrolment, student finance, accommodation or other general topics? If so, please use our contact form and select the relevant topic. This will ensure your enquiry is sent directly to the relevant contact person(s) and can be answered quickly and effectively.
Bachelor of Science (B. Sc.)
On the Bachelor’s degree programme in Computer Science, you will acquire a broad foundation in computer science. From programming and software engineering, through databases and networks, to IT security, you will develop practical skills to design, implement and further develop modern software systems.
About the degree program
Bachelor of Science (B. Sc.)
You will learn how to protect networks, applications and IT systems against digital attacks. From secure software development and penetration testing to digital forensics, you will build up the expertise needed to put cyber security into practice.
About the degree program
Bachelor of Science (B. Sc.)
The Artificial Intelligence Bachelor's programme combines the fields of computer science, data science and modern AI methods to provide a comprehensive education in intelligent systems. Key areas of focus include machine learning, computer vision, natural language processing and practical AI applications.
About the degree program![[Translate to English:]](/fileadmin/_processed_/image-enhancer/53330/20240620_CARISSMA_Gruppe_1-1613x1075-crop-default-285-381-1613-908.webp "[Translate to English:]")
Master of Science (M. Sc.)
On the English-taught Master’s programme in Artificial Intelligence, you will deepen your expertise in modern AI technologies such as deep learning, computer vision and natural language processing. Through practice-oriented projects, cutting-edge research and personalised options, you will be prepared for challenging roles in development, research and international technology sectors.
About the degree programWould you like to develop software and understand how artificial intelligence works? Then the Bachelor’s degree in Computer Science and Artificial Intelligence is exactly the right course for you. In this English-taught programme, you’ll gain a solid foundation in computer science: programming, software engineering, web technologies, database systems, big data and data visualisation will form part of your core curriculum. In addition, you’ll learn about key AI methods – including machine learning, neural networks, natural language processing, heuristic optimisation, computer vision and intelligent agents.
Ethical issues, data protection and the security of digital systems also play an important role. You’ll adopt a scientific approach to your work, develop teamwork skills and learn to present your findings clearly. This broad skill set opens up a wide range of career prospects in software and AI development, data science, IT consultancy or international tech companies – wherever digital solutions are shaping the future.
Graduates with a degree in computer science or AI are in demand wherever information is stored, processed and transmitted – which, nowadays, is across all sectors, companies and organisations.
Career prospects in the job market are correspondingly good. All-round IT professionals are just as sought-after as AI specialists.
You will be prepared, above all, for specialist and managerial roles in the following areas: software development, AI development, data science applications, cloud development, IT project management, IT consultancy and training.
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
| 1. Semester | |
|---|---|
| 2. Semester | |
| 3. Semester | |
| 4. Semester | |
| 5. Semester | |
| 6. Semester | |
| 7. Semester |
Based on the regulations for international orientation and selection procedures, the selection of applicants focuses (in addition to your final grade) particularly on your grades in mathematics and computer science (programming-related) courses. A good score on the SAT also increases your chances of admission.
All necessary information for applying to the CSAI program can be found here: https://www.thi.de/en/studies/application/bachelorapplication-from-abroad.
Students can only start their studies in the winter semester. The application period begins on May 2 and ends on July 15, every year.
We are not a distance learning university. There are no more online classes.
Formal eligibility is determined by uni-assist, not by THI. Here you can find out about your prospective eligibility: https://www.uni-assist.de/en/tools/check-university-admission/. The available study places are then assigned in the order of the grades determined by uni-assist. There is no further aptitude test at THI. Prospective students from China, India, Vietnam or Mongolia please note that before submitting your certificates to uni-assist, you should have your documents certified by the Academic Verification Office of your home country.
Formal proof of proficiency in English is not required, nor is proficiency in German. However, it is strongly recommended that you have both a reasonable level of English and a basic knowledge of German.
Cooperative (dual) study will no longer be supported in this program from the upcoming winter semester.
If you miss entire semesters due to visa delays, you can apply for an extension of exam deadlines. However, this application can only be made in person, after you have arrived at THI. To avoid disadvantages, you should definitely arrive at the beginning of the third semester. You can make up the courses of the missed semesters.
If you have already acquired credit points in a previous study program, a credit transfer is possible. However, it can only be applied for and decided upon after enrollment.
On-campus accommodation (e.g. in dorms) is not usual in Germany. You must look for accommodation on the open housing market on your own responsibility. This applies also for student houses.
There are currently no tuition fees at Bavarian public universities. You only have to pay a semester fee of 67 EUR to the Student Services/Studentenwerk. In return, however, there are discounts, e.g. in the refectory.
As of winter semester 2024/25, THI charges service fees of 500 Euros per semester for students from third countries. If you have any questions, please contact incomings@thi.de
Study places are limited. They are assigned according to the uni-assist grades of applicants with priority given to EU applicants, although their number is small. Exact grades or thresholds cannot be given in advance, as they dynamically result from the applicant situation.
If you arrive late and can prove that you applied for a visa in time, you may request for an extension of the exam deadlines by the amount of time missed. You can only make this request if you are there in person. You can take missed modules (and exams) one year later together with students of the following year. Your duration of study will of course be extended as a result.
If you fail to arrive at THI in person by the end of your third semester, all exams from the first two semesters will be considered failed for the first time.
Info for students who arrive in 3rd semester:
THI regulations state that you need to take every exam for the first time by the end of the 3rd semester, otherwise the exam will count as “failed” for the 1st time (failing an exam 3 times will lead to exmatriculation).
The application for withdrawal and grace period can be found in the PRIMUSS portal under Applications and Messages > Select application/form > Others > General Application.
The application form is empty, i.e. students can explain in detail why they need an extension and the reasons for it. The applications and the corresponding supporting documents (confirmation of receipt/application letter from the visa office/embassy and presentation of the visa in the travel application - copy of identity card with arrival stamp in Germany) should be submitted immediately after entry, ideally together with the change of address (change of address from abroad to German address).
Assuming you have access to Moodle (our Learning Management System), you can sign up for the courses remotely. As they progress in the lecture, lecturers upload materials (Documents etc.) about their respective lecture in the moodle course (some moodle course rooms might even contain all the materials for the whole semester). Thus, you can start studying „from home“ and prepare for the time of your arrival in Ingolstadt.
We also recommend learning German as soon as possible in order to be better prepared for daily life in Germany as well as possible entry into a job market (also as interns during your studies).
You may enroll in a higher semester if your previous degree program is considered equivalent to ours and only if there are free study places due to dropouts. Otherwise, you must start in the first semester, but you can have equivalent modules credited.
An application for credit transfer can only be submitted by enrolled students.
Do you have any questions about applications, admission, enrolment, student finance, accommodation or other general topics? If so, please use our contact form and select the relevant topic. This will ensure your enquiry is sent directly to the relevant contact person(s) and can be answered quickly and effectively.
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