Резултати од пребарување

Табови

Igor Mishkovski Ph.D.

Табови

Gjorgji Madјarov Ph.D.

Intelligent systems (4+1)

1. General Information

 

Postgraduate studies in intelligent systems aim to provide top scientific and research staff to meet the growing demand for highly qualified engineers capable of overcoming the most challenging development, research and process problems. Students in this study program gain knowledge that provides them with great professional flexibility and a wide selection of jobs wherever there is a need to apply problem-solving skills, as well as critical thinking skills, which they will use to solve complex problems. which are in the domain of intelligent systems. Students enrolling and graduating in this field are highly sought after in companies and research centers. In addition to being trained to participate in the development, implementation and maintenance of a variety of applications in the field of intelligent systems, including the business domain, they will have the necessary basis to pursue doctoral studies, as well as to participate in interdisciplinary science projects. and technology.

  • Name of the proposer: University "Ss. Cyril and Methodius University in Skopje, Faculty of Information Sciences and Computer Engineering - FINKI
  • Course Title: Second Cycle Academic Studies in Intelligent Systems
  • Scientific-research area: technical-technological / natural mathematical
  • Field: Computer Science and Informatics / Informatics
  • Areas: Artificial Intelligence , Intelligent Systems, Databases, Numerical Analysis , < / Algorithms , Information Processing, Data Processing, Robotics, Other .
  • The value of postgraduate studies is 60 ECTS or 120 ECTS credits.
  • Duration of studies: 2 or 4 semesters .
  • One academic year consists of two semesters lasting 30 weeks (1 semester = 15 weeks).
  • Admission Requirements : According to the competition announced by the university, completed undergraduate studies in information science, computer or related fields with a minimum of 240 credits. For study directions that carry less than 240 credits, exams offered in the introductory layer are added.
  • Introductory layer : Students who have earned less than 240 credits during their studies are offered a set of differential introductory courses. After their successful realization, the student acquires the right to continue with the first semester of postgraduate studies.
  • First semester: 3 Mandatory courses and 2 electives, one of which may be from the University list.
  • Second semester : 2 electives, one of which can be from the University list (only if in the first semester the subjects are selected at the Faculty level) and a final project - a master's thesis of 18 ECTS. / li>
  • 1 ECTS credit corresponds to 30 hours of total employment.
  • The number of contact hours is 4.
  • The academic title or degree obtained after graduation is

 

-Master of Information Science and Computer Engineering in Intelligent Systems

 

 

2. Studies

Table 3: List of Postgraduate Courses

РБ Subject Semester M / E ECTS
1 Modeling and fusing unstructured data IX M 6
2 Advanced Artificial Intelligence and Machine Learning Topics IX M 6
3 Modern intelligent systems IX M 6
4 Selected from Table 4 IX E 6
5 Selected from Table 4 IX E 6
6 Selected from Table 4 X E 6
7 Selected from Table 4 X E 6
8 Master Thesis X M 18

 

Table 4 shows the electives from the Intelligent Systems 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 course from the university list of free elective courses.

 

Table 4: Electives

РБ Subject Semester ECTS Fund hours
1 Analysis and design of information systems IX 6 4 + 0 + 0 + 0
2 Mobile web services IX 6 4 + 0 + 0 + 0
3 Algorithms and programming in robotics IX 6 4 + 0 + 0 + 0
4 Collective Intelligence IX 6 4 + 0 + 0 + 0
5 Intelligent user interfaces IX 6 4 + 0 + 0 + 0
6 Introduction to Financial Engineering IX 6 4 + 0 + 0 + 0
7 Ambient intelligence IX 6 4 + 0 + 0 + 0
8 Data-based business decision-making systems IX 6 4 + 0 + 0 + 0
9 Robot systems IX 6 4 + 0 + 0 + 0
10 Content based indexing and search IX 6 4 + 0 + 0 + 0
11 Evolutionary calculation X 6 4 + 0 + 0 + 0
12 Agent-based modeling X 6 4 + 0 + 0 + 0
13 Knowledge-based information systems X 6 4 + 0 + 0 + 0
14 Computational game theory X 6 4 + 0 + 0 + 0
15 Representation and approximate knowledge discovery X 6 4 + 0 + 0 + 0
16 Human-robot interaction X 6 4 + 0 + 0 + 0
17 Computationalparadigms in the Internet of Things X 6 4 + 0 + 0 + 0
18 Mathematical methods in robotics X 6 4 + 0 + 0 + 0
19 Collaborative computer systems X 6 4 + 0 + 0 + 0
20 Sensor-robotic systems X 6 4 + 0 + 0 + 0
21 Intelligent mobile applications X 6 4 + 0 + 0 + 0
22 Multimodal interaction X 6 4 + 0 + 0 + 0
23 Discovering knowledge in big graph data X 6 4 + 0 + 0 + 0

 

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 .

Табови

Sonja Gievska Ph.D.

Табови

Milos Jovanovik Ph.D.

Discrete Mathematics 2

Цел на предметната програма: 

To introduce the student to basics of Boolean algebra, its role and application in computer sciences and informatics technologies. To overcome the basic counting techniques and learn how to apply them in solving practical problems. To learn to solve recurrence relations. To introduce students to matrices and matrix algebra and systems of linear equations. To learn the terminology in graph theory and how to apply graphs in modelling and solving practical problems in computer sciences. 

Акредитација: 

Discrete mathematics 1

Цел на предметната програма: 

To introduce students to basic elements of discrete mathematics as a foundation of computer sciences and new technologies. In this context students should learn how to apply the formal methods of propositional and predicate logic in modeling situations from real life including those in the field of computer sciences. To learn and apply basic proof methods and the methods of mathematical induction. To explain with examples the terminology, operations in the theory of sets, functions and relations and their application. 

Акредитација: 

Центар за напредни интердисциплинарни истражувања ЦеНИИс, УКИМ го најавува предавањето:

DNA self-assembly and DNA nanotechnology

Предавач: Проф. д-р Наташа Јоноска
Distinguished University Professor, University of South Florida in Tampa Florida, Fulbright Specialist at FINKI, Research fellow at Center for Advanced Interdisciplinary Research, UKIM

14.05.2024, 18:00, Амфитеатар на ФИНКИ

Модератори: Проф. д-р Ордан Чукалиев, раководител на ЦеНИИс,
проф. д-р Невена Ацковска, ФИНКИ

Предавањето е поддржано од ФИНКИ, Fulbright Specialist Program, Македонска секција на IEEE

 

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DNA self-assembly and DNA nanotechnology 

Abstract: 
Bottom-up self-assembly of DNA nanostructures have been proposed for variety of biotech uses ranging from information storage, to targeted drug delivery or scaffolding for new materials. Engineering predefined building blocks at nano level with various chiralities that assemble in large 3D crystallographic structures is an essential step for both 3D algorithmic assemblies as well as for spatial information storage.  We will discuss some recent developments in the field and will focus on spatial systems as models for information processing at molecular level. The rationally-designed 3D DNA motif, the tensegrity triangle, is the first DNA molecule used to provide DNA crystallographic assemblies. The  possibilities of these building blocks give ever-increasing geometric complexities that form vast arrays of three-dimensional structures. We show a model that explains and predicts which tensegrity triangle structures can form and which chiral topology they can form, left- or right-handed. The theoretical model is also experimentally verified through units designed with incremental rotational steps.

Short biography:

 

Nataša Jonoska is a Distinguished Professor at the Department of Mathematics and Statistics at University of South Florida in Tampa Florida. Her research interests are in theoretical and computational models of molecular self-assembly and molecular biology. She has had extensive research collaborations with experimentalists in molecular biology and structural DNA nano technology. She holds a PhD degree in Mathematical Sciences from the State University of New York in Binghamton NY, USA  and since 2014 she is a Fellow of the American Association for the Advancement of Science. Her work on three-dimensional DNA self-assembly as computing models has been awarded with a Rozenberg Tulip Award in DNA Computing and Molecular Programming by the International Society for Nanoscale Science and Computing. Her work has been/is supported by the National Science Foundation (NSF), National Institute of Health (NIH), the W.M. Keck Foundation and in 2022 she was elected a Simons Fellow in Mathematics. For ten years she served as a Chair of the annual DNA Computing and Molecular Programming conference and co-chaired the annual Unconventional Computing and Natural Computing conference. She also serves on editorial boards of several journals including Theoretical Computer Science, Natural Computing, International Journal of Foundations of Computer Science, and has edited nine books on these topics. In 2021 the Florida section of Mathematical Association of America awarded her with the MAA award for Distinguished College or University Teaching of Mathematics while the journal Theoretical Computer Science published a special issue marking her 60th birthday. She was elected as a foreign member at Macedonian Academy of Sciences and Arts in 2022.

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Табови

Riste Stojanov Ph.D.

Probability and Statistics

Цел на предметната програма: 

Students will be introduced to basic concepts of probability and statistical analyses with their application in computer sciences. The knowledge of this subject is solid support for advanced courses where elements of probability and statistics are applied. 

Акредитација: