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03/04/2019

Во организација на групата Data Science Macedonia и со поддршка на одделите на македонската ИЕЕЕ секција за теорија на информации и за компјутери, како и Факултетот за Електротехника и информациски технологии (ФЕИТ) и Факултетот за информатички науки и компјутерско инженерство (ФИНКИ), на 04.04.2019 година (четврток) со почеток во 18:00 часот во INNOFEIT (анексот на ФЕИТ), ќе се одржи покането предавање на тема:

,,Understanding Health and Nutrition through Data Science“

Предавач: Tome Eftimov, PhD, Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia, Postdoctoral researcher at Stanford University in USA

Апстрактот и биографијата на предавачот се дадени во прилог.

 

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Intelligent Information Systems (4+1)

Intelligent Information Systems (4+1)

1. General information 
 
The science of building artificial intelligent systems takes the central place in the engineering sciences. There are two main approaches in developing artificial intelligent systems: machines that incorporate intelligence – robots, and one that starts with biological systems and leads towards artificial being – bioinformatics and bioengineering. The Intelligent System Engineering is the place where students can understand and develop intelligent systems in solving real-life problems. 
  • Offered by: Ss. Cyril and Methodius University - Skopje, Faculty of Computer Science and Engineering – FCSE
  • Study programme: Intelligent Systems Engineering – bioinformatics / robotics
  • Scientific-research field: Engineering, Natural sciences and applied mathematics
  • Category: Informatics
  • Sub-category: Intelligent systems
  • The master studies cycle consists of 60 ECTS.
  • Study duration: 2 semesters
  • One academic year is divided into two semesters with 30 weeks each (1 semester = 15 weeks)
  • Enrollment requisites: fully completed undergraduate study cycle with a minimum of 240 ECTS with a degree in the fields of computer science and/or computer engineering. In the case of having an appropriate degree with less than 240 ECTS, the student has to enroll the introductory courses first.
  • Introductory courses: only for students that have obtained less than 240 ECTS. A number of differential introductory courses are offered in order to level up the required competences. Upon successful completion of the introductory courses, the student has the right to continue with the formal master study programme courses in the second year of studies.
  • First semester: 3 compulsory courses + 2 elective courses (one of the elective courses can be chosen from the courses list offered by the University)
  • Second semester: 1 compulsory course + 1 elective course (can be chosen from the courses list offered by the University only in the event that this opportunity has not been used in the previous semester) + final master thesis project that equals 18 ECTS.
  • 1 ECTS = 30 hours of work load.
  • Contact hours per week is 4. 
Degree: Master of Intelligent Systems Engineering, module Bioinformatics or Master of Intelligent Systems Engineering, module Robotics
 
2. Studies
 
Table 1: List of courses for master studies
   Professor

Course

Semester

 ECTS
 1   assoc. prof. Slobodan Kalajdziski

 Information systems analysis and design

IX

 6
 2  assoc. prof. Andrea Kulakov  Advanced topics in Artificial Intelligence IX 6
 3  

 Mandatory general-education course

IX

6
 4    Elective course IX 6
 5    Elective course

IX

6
 6  assoc. prof. Vladimir Trajkovikj  Collaborative computer systems X 6
 7    Elective course X 6
 General-education courses
 1    Research methods and writing techniques IX  6
 2    Project management IX  6
 

Табови

Aleksandar Stojmenski, Ph.D.

Табови

Марија Стојчева

Табови

д-р Анастас Мишев

Табови

Magdalena Kostoska Ph.D.

EuroCC

Body: 

eurocc.png

Within the EuroCC project under the European Union’s Horizon 2020 (H2020), participating countries are tasked with establishing a single National Competence Centre (NCC) in the area of high-performance computing (HPC) in their respective countries. These NCCs will coordinate activities in all HPC-related fields at the national level and serve as a contact point for customers from industry, science, (future) HPC experts, and the general public alike. The EuroCC activities—with 33 member and associated countries on board—is coordinated by the High-Performance Computing Center Stuttgart (HLRS). The project aims to elevate the participating countries to a common high level in the fields of HPC, HPDA and artificial intelligence (AI). To this end, the EuroCC project will establish National Competence Centres (NCCs) in the participating countries, which will be responsible for surveying and documenting the core HPC, HPDA, and AI activities and competencies in their respective countries. Ultimately, the goal is to make HPC available to different users from science, industry, public administration, and society.

More information about the project can be found at https://www.eurocc-project.eu/

The role of FCSE, UKIM in the project

FCSE, UKIM is the National HPC Competence Center in Macedonia, taking the leading role in developing the roadmap, capacity building, training as well as the facilitation of access to expertise. It will interface with industry partners to enlarge the HPC application nationwide.

Табови

д-р Марија Михова

Табови

д-р Невена Ацковска

Statistics for data analysis (3+1+1)

1. General Information

This program is designed to train staff with solid statistical knowledge with a focus on the newly recognized field of data science. The curriculum combines rigorous statistical theory with broader practical experience in applying statistical models to data work. Graduates will be in high demand. Most students are expected to be employed as statisticians, analysts and data experts within private and public institutions providing statistical consultations.

  • Name of the proposer: University "Ss. Cyril and Methodius University in Skopje, Faculty of Information Sciences and Computer Engineering - FINKI
  • Title of the study program: Second cycle academic studies in Statistics for Data Analysis
  • Scientific-research area: technical-technological / natural mathematical
  • Field: Informatics / Mathematics
  • Areas: Mathematical Statistics and Operations Research, Data Processing, Applied Mathematics and Mathematical Modeling, Programming, Artificial Intelligence, Algorithms, Information Processing .
  • The value of postgraduate studies is 120 ECTS credits.
  • Duration of studies: 4 semesters .
  • One academic year consists of two semesters lasting 30 weeks (1 semester = 15 weeks).
  • Requirements for enrollment : according to the competition announced by the university, completed undergraduate studies in information science, computer or related fields with a minimum of 180 credits.
  • Introductory Layer : The introductory layer is the first two semesters in which students are offered a set of differential introductory courses. After their successful realization, the student acquires the right to continue with the second year of postgraduate studies.
  • Third semester: 3 Mandatory courses and 2 electives, one of which may be from the University list.
  • Fourth semester : 1 Mandatory course and 1 elective course, the elective course can be from the University list (only if in the first semester the courses are selected at the Faculty level) and the final project - master's thesis from 18 ECTS.
  • 1 ECTS credit corresponds to 30 hours of total work engagement.
  • The number of contact hours is 4.
  • The academic title or degree obtained upon completion of the studies is Master of Science in Information Science - Statistics for Data Analysis

                Master of Science in Informatics - Statistics for Data Analytics

 

2. Introductory layer

The first year of study is the introductory layer for students whose studies lasted less than four years, ie students who gained 180 credits from previous studies. Students must pass differential exams that will enable them to enter the basics of mathematics and computer science needed to successfully complete their studies.

Table 1: List of subjects in the first year of study

РБ CODE / Subject Semester M / E ECTS
1 Mandatory subject 1 from Table 2 VII M 6
2 Mandatory subject 2 from Table 2 VII M 6
3 Mandatory subject 3 from Table 2 VII M 6
4 Mandatory subject 4 from Table 2 VII M 6
5 Elective course 1 * VII E 6
6 Mandatory subject 5 from Table 2 VIII M 6
7 Mandatory subject 6 from Table 2 VIII M 6
8 Mandatory subject 6 from Table 2 VIII M 6
9 Elective course 5 * VIII E 6
10 Selection from the university list of free courses VIII E  

 

Elective courses can be selected from the proposed list of courses of the study program (Table 2), or from the proposed lists of courses from the introductory layer of other study programs of the Faculty of Information Sciences and Computer Engineering. The selection of courses should be made in accordance with the previous knowledge of the candidate and the necessary knowledge to continue with the postgraduate studies in statistics for data analysis. When choosing courses, the student should coordinate with the head of the study program. A free choice of subject is also allowed, which is on the university list of subjects for the first year of two-year postgraduate studies.

After the successful completion of all ten courses and 60 credits, the student with previously acquired 180 ECTS credits (or completed three-year studies) continues with the courses from the second academic year of postgraduate studies - Table 3 (III and IV semester).

  * Selection from the lists of subjects from the introductory layer of all master studies at the Faculty of Information Sciences and Computer Engineering

 

Table 2: List of recommended courses in the first year of study

РБ New code /   Subject Semester ECTS
1 F18L1W011 Discrete Mathematics VII / VIII 6
2 F18L1S013 Calculus VII 6
3 F18L2W006 Probability and statistics VII 6
4 F18L3W035 Linear Algebra and Applications VII 6
5 F18L3W008 Introduction to Data Science VII 6
6 F18L3W161 Social Networks and Media VII 6
7 F18L3W108 Internet of Things VII 6
8 F18L3W004 Databases VII 6
9 F18L3W068 Computing in the Cloud VII 6
10 F18L3S036 Machine learning VIII 6
11 F18L3S150 Data Mining VIII 6
12 F18L3S163 Statistical Modeling VIII 6
13 F18L3S157 Data warehousing and analytics VIII 6
14 F18L1S023 Business Statistics VIII 6
15 F18L3W076 Introduction to time series analysis VIII 6

 

Table 3: List of Postgraduate Courses in Statistics for Data Analysis

РБ CODE / Subject Semester M / E ECTS
1 SNP-Z-1 Data analysis with statistical packages IX M 6
2 SDP-Z-3 Bayesian data analysis IX M 6
3 SDP-Z-4 Data preparation and research IX M 6
4 Elective item from Table 4 IX E 6
5 Elective item from Table 4 IX E 6
6 SNP-Z-2 Regression Models X M 6
7 Elective item from Table 4 X E 6
8 Master Thesis X M 18

 

Table 3 lists the electives from the Statistics for Data Analysis study program. In addition to these courses, the student can choose from all elective courses, defined for all study programs, from the second cycle that are serviced by the faculty. It is allowed to choose one elective subject from the university list nand free electives.

 

 

Table 4: Optional list of offered items

РБ New code /   Subject Semester ECTS
1 Methods of statistical locking IX 6
2 Concepts and application of big data IX 6
3 Analysis and forecasting time series IX 6
4 Advanced algorithms IX 6
5 Modeling and fusing IX 6
6 Information Processing in Biological Systems IX 6
7 Analysis of data from related systems IX 6
8 Text Data Processing IX 6
9 Optimization methods IX 6
10 Data processing in bioinformatics IX 6
11 Network Analysis IX 6
12 Ambiental intelligence IX 6
13 Web of the Future IX 6
14 Statistical Programming X 6
15 Statistical Learning X 6
16 Multidimensional statistical analysis X 6
17 Numerical methods for data science X 6
18 Statistical research skills: editing , reporting and visualization of data X 6
19 Business Analytics X 6
20 Random processes X 6
21 Big Data Modeling and Management X 6
22 Discovering knowledge in big graph data X 6
23 Open and related data X 6
24 Modern Simulations and Modeling X 6
25 Computational paradigms in the Internet of Things X 6
26 Data analysis from mobile sensors / sources X 6
27 Intelligent mobile applications X 6

 

The student can choose a subject from the list of offered elective courses from all study programs of the second cycle of studies. The list of offered electives can be found on this   link .