Master´s program in Artificial Intelligence and Machine Learning

In this research-oriented Master of Science program, students further develop their technical and interdisciplinary competencies in the area of artificial intelligence (AI) building on a preceding computer science bachelor's program. The program qualifies graduates for research and development work in basic research or in industry.


  • Language of instruction: English
  • Scope: 120 credit points need to be acquired during this four semester program
  • Admission: Adequate entry-level skills through completion of a computer science-related degree program with at least 180 CP. Entrance examination may be required – check your eligibility (opens in new tab) !
  • Language requirement: Applicants must provide proof of their English skills: UNIcert level III, TOEFL test (Paper 550, CBT 213, iBT 95), IELTS 7.0, CEFR C1 or equivalent.
  • Start: winter semester recommended, summer semester possible
  • Application: summer term: 01.12.-15.01. | winter term: 01.06.-15.07. | Current deadline dates | Early application recommended!
  • Balanced mix between foundations and hands-on skills via integrated projects
  • Exceptionally broad curriculum in the field of artificial intelligence as well as extensive opportunities to specialise in all areas of machine learning

The main part of the Master's program consists of electives from four areas, which are divided into

  • an area of fundamentals (Foundations of Artificial Intelligence),
  • an area of advanced models and methods of artificial intelligence and machine learning (AI Models and Methods),
  • an area of challenges in the development of real AI systems (AI Systems)
  • and an area of application-oriented courses (AI Domains and Applications).

Students must take courses from all areas.

In addition to a range of seminars and labs, students can prepare for a final Master's thesis at an early stage through larger, possibly multi-part projects.

Set individual priorities

Students have a great deal of freedom in designing their study plan and can thus pursue their individual interests. The elective areas ensure that students acquire the necessary basic knowledge as well as the specialist knowledge and competences essential for the program.


A great strength of the program are the integrated projects and labs. Here, students have the opportunity to participate in challenging projects in cutting-edge international research.

Below you will find the exemplary course of studies. More detailed information on study and examination plans can be found under “Regulations ” at the bottom of the page.

The admission requirement for the English-language Master's program in Artificial Intelligence and Machine Learning (AIM) is the completion of a program that imparts competences amounting to at least 180 credit points (CP). Of these, at least 60 CP must not be significantly different from the acquired entry-level skills imparted in the reference Bachelor's (B.Sc.) degree program in Computer Science at the TU Darmstadt (see also the following sections “Subject-specific requirements” as an excerpt from the program regulations).

Formal entrance examination

The entry-level skills are proven by the certificate of the first degree and the Diploma Supplement or comparable documents submitted with the application.

Material Entrance Examination

If the entry-level skills could not be clearly clarified in this way, a written examination is carried out.

Admission with remedial coursework

If, after the entrance examination, it turns out that the applicants lack entry-level skills which can be compensated for by making up achievements to the extent of no more than 30 CP, admission can be made subject to remedial coursework. This must be completed within the first two semesters. Which modules or subject examinations are made a condition and by when they must be completed will be listed in the notification of admission.

Usual requirements for all study programs

In addition, the usual requirements for all study programs apply: in particular, a timely application with complete documents (or documents submitted later within a deadline) and – after admission by the TU Darmstadt – timely transfer of the semester fee.

The entry-level skills described below are essential for the successful completion of the M.Sc. Artificial Intelligence and Machine Learning program. It is a selection of the most important competences that are taught in the reference Bachelor's (B.Sc.) program in Computer Science at TU Darmstadt and thus also provide the essential prerequisites for the successful continuation of studies in a Master's program that builds on it.

Within the competences from their previous degree to be proven in the amount of at least 180 credit points (CP), the applicants must prove entry-level skills in the amount of a total of 60 CP for admission.

Specific requirements

In the following, the expected subject-specific entry-level skills for the M.Sc. Artificial Intelligence and Machine Learning program are described in detail.

Applicants should be able to use mathematical notations and methods to substantiate concepts of computer science, especially for formal modelling and verification of software and hardware systems.

The reference degree program at TU Darmstadt teaches these initial skills in the courses Automata, Formal Languages and Decidability; Modelling, Specification and Semantics; Propositional and Predicate Logic, among others.

Applicants should be able to,

  • independently select the standard algorithms and data structures required for the solution from a problem description according to the functional and non-functional requirements or construct and assess new algorithms and data structures for solving the problem on the basis of known strategies, if necessary taking parallelism into account.
  • to combine individual components of a programming language independently and without an analogous example within the framework of a programming task into an overall solution.
  • solve programming tasks in different, also parallel, programming languages that follow different paradigms, have different application areas and are located on the whole range of abstraction levels.
  • ensure the quality of the created implementations through formalised test procedures and design methods.
  • apply the aforementioned knowledge in practically relevant areas of computer science themselves. In each case, non-functional aspects, in particular also the security of the IT systems created, should also be taken into account.

These entry-level skills are acquired in the reference program at the TU Darmstadt in the following courses: Algorithms and Data Structures; Computer System Security; Computer Networks and Distributed Systems; introduction to Compiler Construction; Introduction to Artificial Intelligence; Functional and Object-oriented Programming Concepts; Formal Methods in Software Design; Information Management; Operating Systems; Parallel Programming; Probabilistic Methods of Computer Science; Scientific Computing; Software Engineering; Visual Computing.

Applicants should have the ability to

  • independently combine the individual design principles and basic elements of digital circuits, as they are introduced separately one after the other in the lectures, into an overall solution within the framework of a hardware design task without an analogue example.
  • solve design tasks at different levels of abstraction and from different application areas by means of structured design methods in different description languages and using a spectrum of design tools and evaluate them with regard to suitable quality measures.
  • understand the interaction of computer, processor and microarchitectures and make appropriate implementation decisions from this for the system and application software level.

Courses in which these entry-level skills are taught in the reference program at TU Darmstadt are Digital Technology and Computer Organisation.

Applicants need the ability to

  • independently infer from the description of a Computer Science problem that Artificial Intelligence (AI) is required to solve it,
  • identify the required AI approaches, AI standard algorithms and AI representations according to the functional requirements,
  • apply individual design principles and basic methods from artificial intelligence
  • and independently combine them into an overall solution within the framework of an AI system design task without an analogue example.

Solve design tasks in Data Science and Machine Learning at different levels of abstraction and from different areas of application by means of structured design methods in different description languages and using a spectrum of design tools and evaluate them with regard to suitable quality measures.

These entry-level skills are taught in the following courses of the reference Bachelor´s program at TU Darmstadt: Introduction to Artificial Intelligence; Information Management; Probabilistic Methods in Computer Science.

Notes about the application process

Please note that the application process has several required steps:

  • You first need to create an account at the TUCaN application portal.
  • Follow the steps to select your desired study program.
  • Make sure you upload your ID (passport or similar).
  • Note that for international applications, you will need to submit your application once it is complete. Do this as soon as you are done, as the next step depends on it!
  • Once the application has been submitted online – but not before -, the required attachments (“Annex Computer Science” and “Annex Computer Science study courses”) will become available. Click on the “Print link”, or on the “Print” link under “My applications”, and then on “Application or Information”.
  • Fill out the forms appropriately.
  • Note that the application materials need to be submitted “by post” (as it probably says in the application portal). This means that a hard copy has to be sent by a postal or courier service to the address given on the application form. It is neither possible nor sufficient to submit these files electronically!

After completing this program, graduates are able

  • to independently process complex problems and tasks from Artificial Intelligence and Machine Learning with scientific methods, considering different solution approaches, with their improved methodological competence,
  • to implement these competences in new and unfamiliar situations with incomplete information and to think in systemic contexts,
  • to solve tasks and problems with a high level of abstraction and an eye for complex interrelationships,
  • to recognise future problems, technologies and scientific developments and to take them into account appropriately in their work,
  • communicate and present the results of their analyses or the elaborated solutions to different target groups, also in foreign languages,
  • to organise and carry out complex projects efficiently and to work in teams in a goal-oriented manner,
  • to further their professional education independently and to work scientifically to a large extent on their own,
  • in addition, within the framework of the General Education (Studium) Generale), students have expanded their skills and experience in self-selected extracurricular areas.

In summary, the Master's program in Artificial Intelligence and Machine Learning (AIM) develops in students primarily the competence to

  • solve complex problems with incomplete information using approaches from artificial intelligence and machine learning,
  • to develop new methodological approaches and abstract models for Artificial Intelligence and/or Machine Learning,
  • realise new AI systems/ML systems and use them in application problems that cannot be solved with traditional computer science problems.

In addition, there is the skill of being able to increasingly deal with the current research literature as well as the ability to work scientifically in a subfield of AI/ML of one's own choice and to independently solve current problems in practice.

Graduates of the master's program in Artificial Intelligence and Machine Learning face an innovative and promising job market with diverse perspectives in industry, business and research. They can work as development engineers or software developers in research institutions or in areas of research, development and application in companies.

In addition to studying, it is important to gain your own practical experience at an early stage in relevant institutions, to prove your general practical suitability and to establish contacts. The prospect of a good position is increased by the willingness to be professionally mobile, also abroad. Especially in the case of an international orientation, secure English language skills are highly recommended