The goal is to familiarize students with signal analysis and their transfer through linear systems, types of modulation techniques and the effects of noise over modulation techniques.
After the completion of this course, the students will have the knowledge of the architectures with high performance. They will understand the systems that are used for high performance computing and they will have the knowledge for algorithm speedup by their analysis and transformation based on available hardware infrastructure especially on their processor and memory hierarchy.
Students should learn the main concepts of the object oriented analysis and design. To introduce the students to the techniques of refactoring, design patterns and different software architectures. Upon completion of the course the students will be able to identify the restrictions and assess the quality of the software systems. They will be able to evaluate completeness and consistency of software specifications, and to design software architectures according the specific needs.
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.
-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 .
Компанијата Loka (loka.com) ја објавува својата четврта ML пракса за студентите на Финки.Нудиме интересни предизвици од полето на Machine Learning, Data Analysis, Data Mining и Data Processing.Очекуваме студенти кои се заинтересирани да работат со големи податоци и кои сакаат да научат како да користат некои од следните алатки: TensorFlow, Beam, Spark, Flink, Airflow, Docker, Kubernetes, KubeFlow, MLFlow, SageMaker како и некои од Cloud платформите, како AWS, GCP и Azure. Ќе бидете вклучени во изработка на проект за четвртата генерација практиканти, од почеток до крај - анализа на податоците, креирање модел, прием на податоци, податочен pipeline, сервирање на модел и клиентска апликација.
Заинтересираните кандидати може да се пријават на следниот email: hristina@loka.com”
The goal of the course is to introduce the students to basic concepts and modern techniques in the field of data mining. After successfully passing the course the students: will have the inside knowledge about the techniques and algorithms for data mining, as well as the statistical data analysis; will be able to successfully apply the data mining algorithms in solving real problems on large data sets; will be able to conceptualize, analyse, realize, and estimate the performance of a data mining system, and will be introduced to main challenges in the given domain and the domain of research.
