Curriculum

Programme provision

To obtain the MSc degree in Human-Centered Artificial Intelligence, the student must fulfil the following requirements:

  • Have passed General Competence Courses adding up to at least 30 ECTS points
  • Have passed Technological Specialization Courses adding up to at least 30 ECTS points
  • Have performed a Master Thesis of at least 30 ECTS points within the field of the general program
  • Have passed a sufficient number of Elective Courses to bring the total number of ECTS points of the entire study up to 120 

Curriculum

General competence courses

 

Mandatory innovation courses

The mandatory course below combine technological aspects with innovation.

Students must pick one of the following (equivalent) courses:

42500 Innovation in Engineering 5 point January
or
42504 Innovation in Engineering 5 point August
or
42501 Innovation in Engineering 5 point June

Students with advanced innovation competences should take 42502/42503/42505 Facilitating Innovation in Multidisciplinary teams as an alternative to 42500/42501/42504 Innovation in Engineering.

42502 Facilitating Innovation in Multidisciplinary teams 5 point January
or
42505 Facilitating Innovation in Multidisciplinary teams 5 point August
or
42503 Facilitating Innovation in Multidisciplinary teams 5 point June

 

Mandatory Human-Centered and Digital Innovation course

The mandatory course below introduces a human-centered design approach, combined with aspects of digital innovation, and are part of the DTU Compute Digital Innovation Canon.

Students must pick the following course:

02809 UX Design Prototyping 5 point Autumn E1A (Mon 8-12)

 

Mandatory Machine Learning Course

The mandatory course below introduces essential and required aspects of machine learning and data mining.

Students must pick the following course:

02450 Introduction to Machine Learning and Data Mining 5 point Spring F4A (Tues 13-17), Autumn E4A (Tues 13-17)

 

Other general competence courses

Students must choose additionally at least 15 points among the courses listed in the two subgroups below; at least 10 ECTS must come from the first subgroup:

02282 Algorithms for Massive Data Sets 7.5 point Spring F1A (Mon 8-12)
02504 Computer Vision 5 point Spring F3B (Fri 13-17)
02561 Computer Graphics 5 point Autumn E5A (Wed 8-12)
02582 Computational Data Analysis 5 point Spring F2B (Thurs 8-12)
02805 Social graphs and interactions 10 point Autumn E5 (Wed 8-17)
02806 Social data analysis and visualization 5 point Spring F3A (Tues 8-12)
02807 Computational Tools for Data Science 5 point E7 (Tues 18-22)

Students that pick courses from the subgroup below will be credited 5 ECTS in the "other general competence" category and any additional points will count as electives. Thus, for students that pick 1) 38103 X-Tech Entrepreneurship, which is a 10 ECTS course, and/or 2) multiple of the courses below, 5 ECTS counts toward the "other general competence" category and additional points will count as electives.

38102 Technology Entrepreneurship 5 point Autumn E1B (Thurs 13-17)
38103 X-Tech Entrepreneurship 10 point Spring F3 (Tues 8-12, Fri 13-17), Autumn E3 (Tues 8-12, Fri 13-17)
38108 Technology and Innovation Management 5 point Autumn E3B (Fri 13-17)

Technological specialization courses

Choose at least 30 points among the following:
 

02180 Introduction to Artificial Intelligence 5 point Spring F3A (Tues 8-12)
02238 Biometric Systems 5 point June
02266 User Experience Engineering 5 point January
02282 Algorithms for Massive Data Sets 7.5 point Spring F1A (Mon 8-12)
02285 Artificial Intelligence and Multi-Agent Systems 7.5 point Spring F4A (Tues 13-17)
02289 Algorithmic Techniques for Modern Data Models 5 point Autumn E1A (Mon 8-12)
02409 Multivariate Statistics 5 point Autumn E1A (Mon 8-12)
02417 Time Series Analysis 5 point Spring F4B (Fri 8-12)
02443 Stochastic Simulation 5 point June
02455 Experiment in Cognitive Science 5 point Autumn E5A (Wed 8-12)
02456 Deep learning 5 point Autumn E2A (Mon 13-17)
02458 Cognitive Modelling 5 point Autumn E2B (Thurs 8-12)
02460 Advanced Machine Learning 5 point Spring F1B (Thurs 13-17)
02471 Machine learning for signal processing 5 point Autumn E1B (Thurs 13-17)
02476 Machine Learning Operations 5 point January
02477 Bayesian machine learning 5 point Spring F2A (Mon 13-17)
02504 Computer Vision 5 point Spring F3B (Fri 13-17)
02506 Advanced Image Analysis 5 point Spring F5B (Wed 13-17)
02507 Project work within Image Analysis and Computer Graphics 5 point January
02514 Deep Learning in Computer Vision 5 point June
02561 Computer Graphics 5 point Autumn E5A (Wed 8-12)
02562 Rendering - Introduction 5 point Autumn E5B (Wed 13-17)
02563 Generative Methods for Computer Graphics 5 point Spring F1B (Thurs 13-17)
02566 Creating Digital Visual Experiences 10 point Spring F2 (Mon 13-17, Thurs 8-12)
02580 Geometric Data Analysis and Processing 5 point Spring F5B (Wed 13-17)
02582 Computational Data Analysis 5 point Spring F2B (Thurs 8-12)
02614 High-Performance Computing 5 point January
02805 Social graphs and interactions 10 point Autumn E5 (Wed 8-17)
02806 Social data analysis and visualization 5 point Spring F3A (Tues 8-12)
02807 Computational Tools for Data Science 5 point E7 (Tues 18-22)
02808 Personal Data Interaction for Mobile and Wearables 10 point Spring F5 (Wed 8-17)
02830 Advanced Project in Digital Media Engineering 10 point Autumn E5B (Wed 13-17)
02840 Computer Game Programming Fundamentals (DADIU) 15 point Autumn
02841 Computer Game Programming in a Production (DADIU) 15 point Autumn
42081 Staging co-creation and creativity 5 point Autumn E1B (Thurs 13-17)

Note: The course 02460 will not be offered in the 2022/2023 academic year. Students are encouraged to pick courses 02456 Deep learning and/or 02471 Machine learning for signal processing instead. Students that have had these courses prior to enrollment are welcome to discuss their study plan options with the Head of Studies.

Elective Courses
All DTU courses at MSc level may be followed. Master students may choose as much as 10 credit points among the bachelor courses at DTU and courses at an equivalent level from other higher institutions. A number of courses at DTU Compute cover advanced techniques based on applied math in depth. The topics taught in such courses can be highly useful in the development of the services and applications upon which this education is based. Examples include courses in image analysis and data mining which can be useful for advanced input and advanced search facilities, respectively. Courses at DTU Photonics go more in depth with the underlying network and encoding technologies, while courses at DTU Management may supplement the training by focusing on the relations between technology, economics, management and organization, and courses at DTU Health may supplement by additional domain knowledge in health related aspects.