Matrix Analysis and Computations
| Module Number | 18-pe-2070 |
| Degree Programs | MSc ETiT, BSc iST, MSc Wi-ETiT, MSc iCE, BSc/MSc CE |
| Credit Points (CP) | 6 |
| Language | English |
| Form of Teaching | Lectures (3 SWS) and tutorials (1 SWS) |
| Form of Examination | Written exam, duration 120 min |
Students will learn matrix analysis and computations at an advanced or research level.
Teaching Content
This graduate course is a foundation class on matrix analysis and computations, which are widely used in many different fields, e.g., machine learning, computer vision, systems and control, signal and image processing, communications, networks, optimization, and many more.
Apart from the theory this course also covers the design of efficient algorithms and it considers many different examples from the aforementioned fields including examples from social media and big data analysis, image processing and medical imaging, communication network optimization, and written text classification.
Specific topics covered in this course:
- basic matrix concepts, subspace, norms
- linear least squares problems
- eigendecomposition, singular value decomposition, positive semidefinite matrices
- linear system of equations, LU decomposition, Cholesky decomposition
- pseudo-inverse, QR decomposition
- advanced tensor decomposition, advanced matrix calculus, compressive sensing, structured matrix factorization
The materials for the course as well as all current information will be provided in an accompanying Moodle course.
