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Data Mining and Knowledge Discovery (IN2030)

Course 240967808 in WS 2017/8

General Data

Course Type lecture
Semester Weekly Hours 2 SWS
Organisational Unit Informatics 7 - Chair of Theoretical Computer Science (Prof. Esparza)
Lecturers Thomas Runkler
Dates Mon, 08:30–10:00, PH HS1

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks - Data sources, characteristics, and errors - Pre-processing and filtering - Data visualization - Projections (pricipal component analysis, multidimensional scaling, SOM) - Data transformations and feature generation - Correlation and regression - Time series forecasting - Classification (Bayes, Discriminance, Support vector machine, nearest neighbor, learning vector quantization, decision trees) - Clustering (sequential, protype based, fuzzy, relational, heuristic)
Links Course documents
E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

SemesterTitleLecturersDates
WS 2019/20 Data Mining and Knowledge Discovery (IN2030) Runkler, T. Mon, 08:30–10:00, Interims I 102
WS 2018/9 Data Mining and Knowledge Discovery (IN2030) Runkler, T. Mon, 08:30–10:00, Interims I 102
WS 2016/7 Data Mining and Knowledge Discovery (IN2030) Runkler, T. Mon, 08:30–10:00, PH HS1
WS 2015/6 Data Mining and Knowledge Discovery (IN2030) Mon, 08:30–10:00, PH HS1
WS 2014/5 Data Mining and Knowledge Discovery (IN2030) Mon, 08:30–10:00, PH HS1
WS 2013/4 Data Mining and Knowledge Discovery (IN2030)
WS 2012/3 Data Mining and Knowledge Discovery (IN2030)
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