http://courses.cs.taltech.ee/w/index.php?action=history&feed=atom&title=Data_Mining_%28ITI8730_%29 2026-04-30T10:52:39Z Selle lehekülje redigeerimiste ajalugu MediaWiki 1.35.9 http://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730_)&diff=10092&oldid=prev 2021-08-27T10:22:11Z

<p>Uus lehekülg: &#039;Fall 2021/2022 ITI8730: Data Mining and network analysis Old code for this course is IDN0110 Taught by: Sven Nõmm EAP: 6.0 Lectures: Tuesdays 14:00 - 15:30 ICT-315 ...&#039;</p> <p><b>Uus lehekülg</b></p><div>Fall 2021/2022<br /> <br /> ITI8730: Data Mining and network analysis<br /> <br /> Old code for this course is IDN0110<br /> <br /> Taught by: Sven Nõmm<br /> <br /> EAP: 6.0<br /> <br /> Lectures: Tuesdays 14:00 - 15:30 ICT-315<br /> <br /> Labs (practices): Thursdays 16:00 - 17:30 ICT-403<br /> <br /> <br /> Consultation: &#039;&#039;&#039;by appointment only&#039;&#039;&#039; Please do not hesitate to ask for appointment!!!<br /> For communication please use the following e-mail: sven.nomm@ttu.ee<br /> <br /> <br /> ==Overview ==<br /> The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four &quot;super problems&quot; of data mining:<br /> * Clustering<br /> * Classification<br /> * Association pattern mining<br /> * Outlier analysis<br /> <br /> Main topics of the course:<br /> * Data types and Data Preparation<br /> * Similarity and Distances, Association Pattern Mining,<br /> * Cluster Analysis, Classification, Outlier analysis<br /> * Data streams, Text Data, Time Series, Discrete Sequences,<br /> * Spatial Data, Graph Data, Web Data, Social Network Analysis<br /> <br /> ==Evaluation==<br /> *3x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br /> *3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br /> *final exam (gives 40 % of the final grade): Written report on assigned topic + discussion with lecturer.<br /> Exam prerequisites: All 3 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br /> <br /> Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br /> <br /> =Lectures =</div>

Sven