Subject

Massive data mining

Course Title

Massive data mining
Mining Massive Data Sets

Code

F18L3W154

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

7 / Winter

Number of ECTS credits

Teacher

—

Prerequisites for enrolling in the course

Parallel and distributed processing

10.

Objectives of the course programme (competences)

Students will become familiar with data mining and machine learning algorithms and techniques for analyzing very large datasets. The focus will be on distributed platforms as well as on how to define and create algorithms for processing and analyzing very large datasets.

11.

Course content

Introduction to MapReduce, frequent sets and association rules, nearest neighbor search in multi-dimensional data, location-sensitive hashing, dimensionality reduction (SVD and CUR), recommendation systems, clustering, Random Walks with Restarts, supervised learning on massive datasets (K-nearest neighbors, perceptron, classification and regression trees, data stream mining, web advertising

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 30 + 15 + 19 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	30 hours

16.

Other forms of activities

16.1.	Project assignments	19 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	19 points
17.3.	Activities and learning	0 points
17.4.	Final exam	60 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

completed activities

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

30+65+30+20+35 = 180 hours

22.

Literature

22.1.	Required literature No. Author Title Publisher Year
22.2.	Additional literature No. Author Title Publisher Year