astronomy, geology and medicine require intelligent methods to obtain
high fidelity images from noisy, incomplete data. The theoretical and
mathematical framework in which imaging and data analysis methods are
be information theory
to which these
lectures will introduce first (first 1/3 semester, suited for Bachelor
and Master students, 3 ETCS). Based on this, information theory for
fields will be developed, which can
be used to reconstruct signals from data (remaining 2/3 semester, more targeted at Master students, 6 ETCS).
The following topics will be addressed:
Aristotelian logic, Bayesian logic, measurement process (signal, response, noise), noise statistics (Gaussian, Poissonian), information measures (entropy, cross-information, ...), information Hamiltonian, relation to thermodynamics and statistical mechanics, theory of Wiener filter, construction of optimal non-linear filters, information field theory.
Lecture notes + 23.6.2015 additions + recent additions + extra notes + more extra notes (to Sec. II.3) (handwritten, with typos, & errors, will be modified during lecturing)Bonus system
Exercise sheets: No. 1, No. 2, No. 3, No. 4, No. 5, No. 6, No. 7,No. 8,No. 9,No. 10,No. 11,Previous examinations:Examination 2015 (Information Theory) Examination 2013, Examination 2012 (yes 2012, despite different date information in the document) , Examination 2011