AIDC – Artificial Intelligence and Distributed Computing

Which is the main aim?

The main aim of the Artificial Intelligence and Distributed Computing master program is to offer knowledge and skills such that the graduates will be able to design and implement systems based on artificial intelligence methods and parallel and distributed approaches.

What topics are studied?
  • Cloud Computing, High Performance Computing;
  • Architectures for Parallel Computing;
  • Intelligent and Multi-agent Systems;
  • Automated Reasoning and Automated Theorem Proving;
  • Machine Learning, deep learning models.

Which is the target group? To this MSc program can apply graduates of:
  • Informatics/Computer Science, Mathematics and informatics;
  • Computer Engineering, Information Technologies;
  • Other programs on scientific or engineering fields.

Which are the career opportunities?
  • Cloud Computing specialist;
  • Artificial Intelligence/ Machine Learning specialist
  • Data Analyst;
  • Researcher in the field of Artificial Intelligence, Distributed Systems, Parallel Computing.
* Note: for syllabus click on the lecture name

Year I Semester I

Topics
ECTS
Lectures / week
Seminaries or Laboratories / week
Distributed Systems
6
2
1
Advanced Logical and Functional Programming
6
2
1
Operational Research and Optimization
6
2
1
Data analysis in R
5
1
2
Architectures for Parallel Computing
5
2
1
Ethics and Academic Integrity
2
1
–
TOTAL
30
10
5
ADDITIONAL TOPICS (FROM UNDERGRADUATE CURRICULA)
Programming I
5
2
2
Logical and Functional Programming
5
2
2

Year I Semester II

Topics
ECTS
Lectures / week
Seminaries or Laboratories / week
Parallel Computing
6
2
1
Term Rewriting
6
2
1
Multi-agent Systems
6
2
1
Automated Theorem Proving
6
2
1
Elective Course
6
2
1
LIST OF ELECTIVE COURSES (to select one out of three)
Network security models and arhitectures
Data Mining
Modelling and Verifying Algorithms in Coq
TOTAL
30
10
5
ADDITIONAL TOPICS (FROM UNDERGRADUATE CURRICULA)
Operating Systems
5
2
2
Programming II
5
2
2

Year II Semester I

Topics
ECTS
Lectures / week
Seminaries or Laboratories / week
Machine Learning
5
2
1
Resource Management in Distributed and Parallel Systems
5
2
1
Techniques for Scientific Work
5
2
1
Metaheuristic Algorithms
5
2
1
Research practice
5
–
3
Elective Course
5
2
1
LIST OF ELECTIVE COURSES (to select one out of three)
Algorithm Synthesis And Mathematical Theory Exploration
Distributed Methods and Technologies based on XML
Special Topics In Artificial Intelligence
TOTAL
30
10
8

Year II Semester II

Topics
ECTS
Lectures / week
Seminaries or Laboratories / week
Research practice
8
–
3
Thesis Preparation
15
–
8
Scientific Seminar
7
–
3
TOTAL
30
–
14
ADDITIONAL TOPICS (FROM UNDERGRADUATE CURRICULA)
Intelligent Systems
5
2
1