SYLLABUS
University: Technical University of Košice
Faculty: Faculty of Electrical Engineering and Informatics
Department: Department of Computers and Informatics
Course Number: 26001223 Course Name: Smart Systems in Cybersecurity
Type, scope and method of learning activities:
Course Type: Lecture, Laboratory exercise
Recommended scope of the course content (in hours):
Full-time study (hours per week): 2,2
Part-time study (hours per semester): 26,26
Study Method:
Number of credits: 6
Recommended semester of study: WT
Recommended semester Study programme Study grade Study Method
3.rok WT Cybersecurity (KB_Bc_D_sk) Bachelor Attendance
Level of study:
Prerequisites:
Course completion requirements:
Assessment and completion of the course: Credit test and examination
Continuous assessment: Student passes the continuous assessment and receives credits when he or she meets the requirement to obtain at least 21% out of 40%.
Credit test
Final assessment: Student passes the final assessment and passes the examination when he or she meets the requirement to obtain at least 31% out of 60%.
Examination
Overall assessment: Overall assessment is the sum of the assessments obtained by students in the assessment period. The overall result is determined in accordance with the internal regulations of the Technical University in Košice. (Study Regulations, the internal regulation principles of doctoral studies)
Learning outcomes:
The student will become familiar with the basic principles, individual steps, and algorithms of data processing and machine learning. The subject aims to acquire practical knowledge for solving standard data mining problems and using data mining tools. The student will gain the understanding necessary for practical applications such as spam filter design, customer behavior analysis (basket analysis), facial recognition, and detection of illegal banking operations. The student will also acquire basic skills for solving selected practical problems (simple computer games and logical puzzles) using search problems (A*, UCS, and others). The student will analyze the performance of individual algorithms from the point of view of time and memory requirements. Based on the acquired knowledge, he selects a set of algorithms for selected problems, analyzes them, implements them, and performs a complex comparative analysis.
Brief course content:
1. Introduction to machine learning. Data processing tools.
2. Machine learning with a teacher (perceptron, method of support vectors, decision trees, k-nearest neighbors).
3. Data preprocessing (imputation, scaling).
4. Validation and evaluation of models, tuning of model parameters (cross-validation, measurement of model performance).
5. Clustering (K-means, DBSCAN).
6. Selection of variables, reduction of high dimensions and practical aspects of the design of intelligent systems.
7. Agent systems – types of agents, state space and agent decision-making.
8. Search algorithms without additional information - depth-first search (DFS), bread-first search (BFS), uniform-cost search (UCS) and iterative deepening.
9. Search algorithms with additional heuristics - Greedy and A* type search, design of admissible heuristics.
10. Solving identification problems with boundaries using agent systems - algorithms such as backward search, forward filtering and edge consistency.
11. Stochastic optimization algorithms and their use in solving identification problems with boundaries - optimization algorithm ACO (Ant Colony Optimization), hill-climbing, simulated annealing and local search.
12. Hybrid approaches (application of biology-inspired algorithms with multi-agent systems).
Recommended Reference Sources:
Python for Data Analysis, Wes McKinney, O'Reilly Media, 2012
Machine Learning: An algorithmic perspective, Stephen Marsland, CRC Press, 2015
Agent Based and Individual based modelling, Steven F. Railsback and Volker Grimm, Princeton Press, 2012
Artificial Intelligence for Humans, Jeff Heaton, Heaton Research, 2014
Recommended optional program components:
Languages required for the course completion:
Notes:
Course assessment:
Total number of students assessed: 0
  A B C D E FX  
  0% 0% 0% 0% 0% 0%  
Teacher:
prof. Ing. Juraj Gazda, PhD.
prof. Ing. Peter Drotár, PhD.
Last modified: 01.09.2022
Approved by: person(s) responsible for the study program