- Statistics (10015)
Course summary:
Abstract:
Statistical analysis is a basic tool needed by every researcher. In this course, we will learn how to draw statistical conclusions from a dataset.
We will learn to compare distributions of different data types and sample groups.
We will learn parametric and non-parametric tests, we will learn to conduct variance analysis, correlation tests and linear regression.
The topics will be studied in a theoretical way and will be practiced on representative examples using R programming language in the computer lab.
- Computational Models (10139)
Course summary:
Abstract:
Students will learn about models of computing machines: finite automata, pushdown automata, and Turing machines. Students will demonstrate knowledge of Formal languages, their descriptions, and their relationships to the computational models; Students will learn the limits of the various models.
- Data Structures and Algorithms (10805)
Course summary:
Abstract:
• Recursion, Sorting and Searching collections, Complexity of Algorithms, graph theory. Data analysis and data processing with python pandas package.
- Robotics and Reinforcement Learning (65026)
Course summary:
Abstract:
Multi-agent systems (MAS) is a subfield of artificial intelligence that investigates systems composed of multiple interacting intelligent agents. These agents can be software programs, robots, or humans, and they operate in a shared environment. The study of MAS focuses on the design, analysis, and implementation of such systems, particularly on understanding the interactions, cooperation, and competition among agents. Key research areas include agent modeling, communication protocols, coordination mechanisms, and the application of MAS to real-world problems.