TECHNICAL UNIVERSITY OF DENMARK · DENMARK
Advanced Topics in Machine Learning: Tensor Networks for Machine Learning
Master's · Taught in en
At a glance
6 mo
Duration
—
IELTS
€8k
Tuition / yr
en
Language
Master's
Level
- August 24-28, 2026Course Dates
- 2.5 ECTS pointsAcademic Credits
- PythonRecommended Programming Language
- International SpeakersConfirmed Faculty
- Report SubmissionAssessment Method
This intensive five-day course delves into the theoretical and practical aspects of Tensor Networks specifically for Machine Learning applications. You will explore technical details, programming techniques, and real-world applications through lectures, tutorials, and exercises. The course is designed for individuals with a solid foundation in machine learning, statistical modeling, mathematics, and computer science, and requires programming experience, ideally in Python. Participants will complete the course by submitting a report on the topics covered. This is a short, focused course, offering 2.5 ECTS points.
Curriculum
The course program covers a range of topics related to tensor networks and their application in machine learning. It includes foundational concepts, advanced techniques, and specific applications across various fields.
- Course Week
- Introduction to tensors and tensor networks
- Applications of tensor decompositions to life science data
- Supervised and Reinforcement learning using tensor networks
- Tensor networks for science
- Deep learning and tensor networks
- Probabilistic circuits and tensor networks
- Tensor networks, divergences and deformed algebra for probability estimation
- Information geometry and tensors
- Project work and closing remarks
Careers
This course focuses on advanced topics in machine learning and tensor networks, preparing participants for roles in research and development within data science and related fields.
- Machine Learning Researcher
- Data Scientist
- AI Specialist
Frequently asked questions
- What are the application deadlines for this course?
- Applications must be submitted by June 14, 2026. You will receive confirmation of your application by June 27, 2026.
- Who is eligible to apply for this course?
- The course requires a solid understanding of machine learning, statistical modeling, mathematics, and computer science, along with programming experience, ideally in Python.
- What are the fees for this course?
- Academics (Master's and PhD students) and DTU-affiliated students are exempt from registration fees for this specific course. Fees will apply for non-academics and participants not affiliated with DTU.
- How long is the course and what academic credit does it offer?
- This is an intensive five-day course, running from August 24-28, 2026, and offers 2.5 ECTS points.
- What programming skills are recommended for this course?
- Programming experience is required, ideally in Python. Participants are also required to bring their own laptop computer.
- What are the potential career outcomes after completing this course?
- This course prepares participants for roles such as Machine Learning Researcher, Data Scientist, and AI Specialist within research and development.
Entry requirements
General KnowledgeSolid understanding of machine learning, statistical modeling, mathematics, and computer science is required. Programming experience, ideally in Python, is also necessary.
Participants are required to bring their own laptop computer.
Fees
Tuition (international)Tuition fees for Master's programs are 7500 EUR per semester, totaling 30000 EUR for a 2-year program. Please note that fees are subject to revision for the Autumn 2026 intake. As this is a short course, fees for non-academics and non-DTU students will apply; academics (Master's and PhD students) and DTU-affiliated students are exempt from registration fees for this specific course.
Tuition (home)Academics (Master's and PhD students) and students affiliated with DTU can use the course planner to register without a fee for this course. For EU/EEA citizens enrolled in degree programs, fees are covered by the Danish state.
LivingNot specified.
For non-academics and participants not affiliated with DTU, a course fee will be charged in addition to registration. Details for guest PhD students are available. Funding for this course is provided by the Novo Nordisk Foundation and the Danish Data Science Academy.
How to apply
1. Submit ApplicationSend your CV to kazfu@dtu.dk no later than June 14, 2026.
2. Confirmation of ApplicationYou will receive a confirmation of your application by June 27, 2026.
3. Formal RegistrationAfter receiving a positive confirmation, you must complete the registration in DTU's systems. For academic participants (Master's and PhD students), this may involve using the course planner or a specific registration form for guest PhD students outside DTU. Other participants will need to complete an additional registration via the continuing education portal.
Scholarships
Novo Nordisk FoundationFunding provided
Danish Data Science AcademyFunding provided
Frequently asked questions
What are the application deadlines for this course?
+
Applications must be submitted by June 14, 2026. You will receive confirmation of your application by June 27, 2026.
Who is eligible to apply for this course?
+
The course requires a solid understanding of machine learning, statistical modeling, mathematics, and computer science, along with programming experience, ideally in Python.
What are the fees for this course?
+
Academics (Master's and PhD students) and DTU-affiliated students are exempt from registration fees for this specific course. Fees will apply for non-academics and participants not affiliated with DTU.
How long is the course and what academic credit does it offer?
+
This is an intensive five-day course, running from August 24-28, 2026, and offers 2.5 ECTS points.
What programming skills are recommended for this course?
+
Programming experience is required, ideally in Python. Participants are also required to bring their own laptop computer.
What are the potential career outcomes after completing this course?
+
This course prepares participants for roles such as Machine Learning Researcher, Data Scientist, and AI Specialist within research and development.