Machine Learning at Scale - Specialization

Specialization: Machine Learning at Scale

"Machine Learning at Scale" focuses on efficiently designing and implementing machine learning algorithms and systems and on methods for analysing data.

To fulfill the requirements for the specialization, the student must follow the requirements in the general curriculum for the programme such that at least 35 ECTS points are obtained among the recommended courses below.

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.

Recommended courses

Choose at least 35 ECTS among the following courses:

02282 Algorithms for Massive Data Sets 7.5 point Spring F1A (Mon 8-12)
02289 Algorithmic Techniques for Modern Data Models 5 point Autumn E4B (Fri 8-12)
02456 Deep learning 5 point Autumn E2A (Mon 13-17)
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)
02582 Computational Data Analysis 5 point Spring F2B (Thurs 8-12)
02613 Python and High-Performance Computing 5 point Spring F5A (Wed 8-12)
02614 High-Performance Computing 5 point January
02830 Advanced Project in Digital Media Engineering 10 point Autumn E5B (Wed 13-17)

Addititional relevant courses

Although not part of the requirements for the specialization, the following courses may also be relevant for the student:
 

02249 Computationally Hard Problems 7.5 point Autumn E3A (Tues 8-12)
02409 Multivariate Statistics 5 point Autumn E1A (Mon 8-12)
02612 Constrained Optimization 5 point Spring F4A (Tues 13-17)