227-0105-00L  Introduction to Estimation and Machine Learning

SemesterAutumn Semester 2020
LecturersH.‑A. Loeliger
Periodicityyearly recurring course
Language of instructionEnglish


AbstractMathematical basics of estimation and machine learning, with a view towards applications in signal processing.
ObjectiveStudents master the basic mathematical concepts and algorithms of estimation and machine learning.
ContentReview 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 notesLecture notes will be handed out as the course progresses.
Prerequisites / Noticesolid basics in linear algebra and probability theory