The most popular new courses – Introduction to Artificial Intelligence
One of the new core courses in the undergraduate program Engineering Management is the Introduction to Artificial Intelligence. The aim of this course is to understand contemporary trends in the development of algorithms and techniques in the field of machine learning and artificial neural networks, as well as to acquire the knowledge necessary for the application of artificial neural networks. The students will also develop the ability to simulate and deploy software tools in the domain of artificial intelligence.
Upon completion of the course program, the students will be able to: apply existing algorithms and implement machine learning systems; define simple problems and ways to solve them using existing software tools; distinguish between types of neural networks, their advantages and disadvantages; identify the application of neural networks in intelligent computer systems.
Some of the most important topics are: Machine Learning; Central Area of Research in Artificial Intelligence; The difference between machine learning and data exploration; Machine learning types; Supervised learning; Regression problem (linear and logistic regression); Classification problem; Structural prediction; Parametric and non-parametric models; Unsupervised learning; Semi-supervised learning; Learning with support; Artificial neural networks; Basic properties, architectures and algorithms of artificial neural network training; Models of neurons; Types of activation functions; Neural network architectures; Training of neural networks; Perceptron; Intelligent agents as a basis for the development of intelligent systems; Artificial neural networks in intelligent systems; Neurocomputing projects; Basic properties, stages, planning and management.
Lab/excersises include: Introduction to artificial neural network simulation software; Recognition systems, artificial neural network simulation, mobile robot motion simulation (examples); Design of projects related to simulation of real systems and application of artificial neural networks.