Faculty subject Artificial Intelligence.
What is intelligence, what is learning and relation manmachine
Overview of machine learning (ML) methods and their
basic principles
Overview of search algorithms
Evaluation of ML
Evaluating attributes
Decision trees, NB and K-NN
Artificial neural networks
Problem solving and heuristic search (A*, RBFS,
minimax)
Knowledge representation, reasoning, decision support
systems
Intelligent robots and agents
Natural language processing
Evolutional computation
Probabilistic modelling
Reinforcement learning
The goal of the course is the students to become acquainted with the field of artificial intelligence and its methods, which includes a collection of tools and approaches for solving problems which are difficult or unpractical to tackle with other methods. Students will practically apply the theoretical knowledge on real problems from scientific and business environment. The students shall be able to decide for a given problem which of the presented techniques should be used, and to develop a prototype solution.
developing skills in critical, analytical and synthetic thinking, the ability to understand and solve professional challenges in computer and information science, the ability of professional communication in the native language as well as a foreign language, the ability to apply acquired knowledge in independent work for solving technical and scientific problems in computer and information science, familiarity with research methods in the field of computer science.
using basic machine learning algorithms preprocessing data for data mining feature subset selection evaluation of decision models using data mining systems using optimizations packages with evolutionary techniques text analysis and text mining using reinforcement learning tools
Expertise in several techniques and methods, used in the field of artificial intelligence. The ability for analysis, synthesis and anticipation of solutions and their consequences for target problems using the scientific methodology.
The use of the presented methods on target problems from scientific and business environment. The understanding and usage of tools in the field of artificial intelligence.
The recognition and understanding of the meaning of basic mathematical and statistical knowledge, the relation between theory and its application in concrete examples of intelligent modelling and artificial intelligence. Autonomy, (self) criticalness, (self) reflexivity, aspiration for quality.
The transfer of the learned principles for planning of
large systems where the principles of artificial intelligent
solutions help to improve the usability and the system
performance. The ability to receive, select and evaluate
new information and proper interpretation in a context.
A self-control and ability to manage limited time when
preparing, planning and implementing plans and
processes. Team work, writing of reports and papers,
public presentations.
Coherent mastering of basic knowledge, gained through
mandatory courses, and the ability to combine the
knowledge from different fields and to apply it in
practice.