227-0105-00L Introduction to Estimation and Machine Learning
Semester | Autumn Semester 2020 |
Lecturers | H.‑A. Loeliger |
Periodicity | yearly recurring course |
Language of instruction | English |
Abstract | Mathematical basics of estimation and machine learning, with a view towards applications in signal processing. |
Objective | Students master the basic mathematical concepts and algorithms of estimation and machine learning. |
Content | Review of probability theory; basics of statistical estimation; least squares and linear learning; Hilbert spaces; Gaussian random variables; singular-value decomposition; kernel methods, neural networks, and more |
Lecture notes | Lecture notes will be handed out as the course progresses. |
Prerequisites / Notice | solid basics in linear algebra and probability theory |