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 Cursus: 201700080
 201700080Information Theory and Statistics
 Cursus informatie
Cursus201700080
Studiepunten (ECTS)5
CursustypeCursus
VoertaalEngels
ContactpersoonM.C. de Jongh
E-mailm.c.dejongh@utwente.nl
Docenten
 Examinator dr.ir. J. Goseling Docent M.C. de Jongh Contactpersoon van de cursus M.C. de Jongh
Collegejaar2022
Aanvangsblok
 2A
AanmeldingsprocedureZelf aanmelden via OSIRIS Student
Inschrijven via OSIRISJa
 Cursusdoelen
 body { font-size: 9pt; font-family: Arial } table { font-size: 9pt; font-family: Arial } After passing the assessment of this course a student: knows Shannon entropy and mutual information, is able to perform computations involving these information measures and is able to estimate these measures from a data set, knows the Huffman, Lempel-Ziv and CTW data compression algorithms, and understands the limits on data compression, understands the connection between machine learning and data compression and is able to quantify performance limits on machine learning algorithms, is able to quantify the optimal performance that can be expected from a classification system in terms of information measures.
 Inhoud
 body { font-size: 9pt; font-family: Arial } table { font-size: 9pt; font-family: Arial } Information theory is a mathematical theory dealing with the fundamental principles of storing, processing, and transmitting data. The first half of this course covers the core concepts of information theory, including entropy and mutual information. These are then used to derive fundamental limits on data compression and communication. The second half of this course focuses on applications of information theory to statistics and machine learning. In particular, information theory will be used to develop performance limits on machine learning algorithms. Assessment 1) Written exam (60 %, needs >= 5.5) 2) homework (20 %) 3) report, based on project or reading assignment (20 %)
Voorkennis
 Basic probability theory, for instance from:• B-AM M4,• B-EE M8,• B-CS/BIT M4 or• 202001177 Probability Theory and Statistics (premaster M-CS).
 Participating study
 Master Computer Science
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 Master Applied Mathematics
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 Master Electrical Engineering
Verplicht materiaal
Handouts
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