Semester | Sommersemester 2015 |
Modulnummer | INF-KM-32 |
Veranstaltungsnummer | INF-KM-43 |
Studiengänge | Informatik Master, Informations-Systemtechnik Master, Wirtschaftsinformatik Master |
IBR Gruppe | CM (Prof. Wolf) |
Art | Vorlesung & Übung |
Dozent | |
LP | 5 |
SWS | 2+1 |
Ort & Zeit | Lecture: Wednesday, 11:30-13:00 Raum IZ-358 Exercise: Wednesday, 13:15-14:45, Raum IZ-358 This lecture is discontinued. If you are interested in Machine Learning and Applications please consider the Lecture Machine Learning for Computer Security (Institute of System Security). |
Beginn | Mittwoch 15. April, 11:30-13:00 |
Hörerkreis | Studenten der Informatik und Elektrotechnik, Wirtschaftsinformatik, Informations-Systemtechnik, Medienwissenschaften, Studenten mit Nebenfach Informatik |
Voraussetzungen | keine |
Scheinerwerb | Successful oral examination. Furthermore, active participation in the exercises is expected. |
Inhalt | Lecture slidesDiese Unterlagen sind nur für registrierte Teilnehmer zugänglich. Um sich zu registrieren, benötigen Sie entweder einen IBR-POSIX-Account, den Sie vielleicht bereits im Rahmen einer Arbeit am Institut erhalten haben, oder einen selbst aktivierten IBR-y-Account. Anschließend melden Sie sich über die Login-Funktion dieser Website an. Kapitel Folien Filme Übungen 0. Organisation 1. Introduction 2. Rule based 3. Decision trees 4. Regression 5. Random search 6. High dimensional data 7. Artificial Neural Networks 8. Instance-based learning 9. Probabilistic graphical models 10. Topic models 11. Unsupervised learning 12. Anomaly detection, recommendersystems, online learning Exkurs: Trendmining (Dr. Olga Streibel) Exkurs: Clustering and density-based clustering (Nguyen Thach) LectureWe investigate popular algorithms applied for supervised and unsupervised machine learning on real data and consider test-set design principles, model and feature selection as well as performance metrics. The discussed considered cover linear/logistic/multivariate/multivariable regression, Support vector machines, artificial neural networks, k-nearest neighbour, k-means, self organizing maps, decision trees, naive Bayes and Bayesian networks, hidden markov models, conditional random fields and principle component analysis. The course will address selected topics in Pervasive Computing with a special focus on Machine learning and activity recognition from sensor-data. In addition, other fields of Pervasive Computing are covered to provide students with a good overview on current advances and research challenges. Depending on the interest of the students, the emphasis on these additional topics may differ.
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Technische Universität Braunschweig
Universitätsplatz 2
38106 Braunschweig
Postfach: 38092 Braunschweig
Telefon: +49 (0) 531 391-0