Credits: 5

Schedule: 12.09.2016 - 20.10.2016

Teaching Period (valid 01.08.2018-31.07.2020): 

I - II (Autumn)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

The students will familiarize themselves with basic data-mining principles and methods. The course will cover different problem scenarios, such as, pattern discovery, clustering, and ordering, as well as, analysis of different types of data, such as, sets, graphs, and sequences. The students will develop their analytical tehniques to cope with challenging
data-analysis problems. They will also develop their practical skills through programming assignments and experimentation with real data.

Content (valid 01.08.2018-31.07.2020): 

The course covers general topics in data mining, such as pattern discovery, similarity search, data clustering, graph mining, ranking and ordering problems, stream computation, and distributed analysis of data, such as map-reduce.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Take-home homeworks, programming assignments, and in-class final exam.

Workload (valid 01.08.2018-31.07.2020): 

24 + 12 (4 + 2)

Study Material (valid 01.08.2018-31.07.2020): 

Lecture slides and online lecture notes.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

T-61.5060 Algorithmic Methods of Data Mining

Prerequisites (valid 01.08.2018-31.07.2020): 

Basic mathematics, statistics, and basic courses on algorithms design.

Grading Scale (valid 01.08.2018-31.07.2020): 

0-5

Description