Machine Learning - Specialization
Machine Learning
Machine learning plays an more and more important role in many engineering application fields. This specialization provides a theoretical and computational foundation in machine learning.
Specialization specific courses
The student must follow the requirements in the general curriculum for the programme. In addition, at least 20 ECTS points are obtained among the following courses:
| 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) |
| 02477 | Bayesian machine learning | 5 | point | Spring F2A (Mon 13-17) |
| 02501 | Advanced Deep Learning in Computer Vision | 5 | point | Spring F4A (Tues 13-17) |
| 02504 | Computer Vision | 5 | point | Spring F3B (Fri 13-17) |
| 02516 | Introduction to Deep Learning in Computer Vision | 5 | point | Autumn E5B (Wed 13-17) |
Recommended elective courses
The following courses are recommended for the specialization:
| 02195 | Quantum Algorithms and Machine Learning | 5 | point | Spring F2A (Mon 13-17) |
| 02456 | Deep learning | 5 | point | Autumn E2A (Mon 13-17) |
| 02476 | Machine Learning Operations | 5 | point | January |
| 02510 | Deep learning and data engineering for image analysis | 5 | point | Spring F5A (Wed 8-12) |
| 02517 | Responsible AI: Algorithmic fairness and explainability | 5 | point | Autumn E2B (Thurs 8-12) |
| 02582 | Computational Data Analysis | 5 | point | Spring F2B (Thurs 8-12) |
| 02611 | Optimization for Data Science | 5 | point | Spring F5B (Wed 13-17) |
Examples of study plans
Four examples are shown: two focusing on machine learning and two focusing on computer vision. Both include study plans for students beginning in September or in February.
The study plans are examples only. Students are free to exchange the shown courses with other courses if the general requirements for the curriculum in mathmatical modelling and computation and the specialisation are satisfied. The proposed plans fulfil requirements of the MMC curriculum.
Study plan with focus on machine learning - September start
Innovation in Engineering (Polytechnical... Innovation in Engineering (Polytechnical Foundation)
Quantitative methods to assess... Quantitative methods to assess sustainability (Polytechnical Foundation)
Study plan focus on machine learning - February start
Quantitative methods to assess... Quantitative methods to assess sustainability (Polytechnical Foundation)
Innovation in Engineering (Polytechnical... Innovation in Engineering (Polytechnical Foundation)
Study plan focus on computer vision - September start
Quantitative methods to assess... Quantitative methods to assess sustainability (Polytechnical Foundation)
Innovation in Engineering (Polytechnical... Innovation in Engineering (Polytechnical Foundation)
Study plan focus on computer vision - February start
Quantitative methods to assess... Quantitative methods to assess sustainability (Polytechnical Foundation)
Innovation in Engineering (Polytechnical... Innovation in Engineering (Polytechnical Foundation)
Specializations are merely recommended ways of choosing the courses in the curriculum. Applicants are not admitted to a specialization but to the programme and it is possible to choose among all the courses in the curriculum following the directions given. However, if a specialization has been fulfilled the title of the specialization may be added to the diploma.
Contact
Tommy Sonne Alstrøm Associate Professor Phone: +45 45253431 Mobile: +45 93511848 tsal@dtu.dk
Anders Bjorholm Dahl Professor, Head of Section Mobile: +4551896913 abda@dtu.dk