Mining Massive Data Sets

Course title

Code

F18L3W154

Study programme

—

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

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

Паралелно и дистрибуирано процесирање

10.

Course objectives (competences)

The course will introduce the students data mining and machine learning algorithms for analyzing massive amounts of data. The emphasis will be on the distributed platforms and Map Reduce as a tool for creating parallel algorithms that can process large amounts of data.

11.

Course content

Introduction and MapReduce. Association Rules: Frequent itemsets and Association rules, Near Neighbor Search in High Dimensional Data, Locality Sensitive Hashing (LSH), Dimensionality reduction: SVD and CUR, Recommendation Systems, Clustering, Random Walks with Restarts, Large scale supervised machine learning: k-nearest neighbor, Perceptron, Classification and regression trees, Mining data streams, 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 the available time

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

15.

Forms of teaching activities

15.1.	Lectures – theoretical teaching	30 hours
15.2.	Exercises (laboratory, auditory), seminars, team work	30 hours

16.

Other forms of activities

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

17.

Grading

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	19 points
17.3.	Activity and participation	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.

Requirements for signature and taking the final exam

completed activities

20.

Language of instruction

Macedonian and English

21.

Method of monitoring teaching quality

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

22.

Literature