Associative and Network Analysis

Major: Artificial Intelligence
Code of subject: 7.122.04.E.035
Credits: 5.00
Department: Artificial Intelligence Systems
Lecturer: R.Ya.Kosarevych
Semester: 2 семестр
Mode of study: денна
Learning outcomes: As a result of the study of the discipline, the student should be able to demonstrate the following learning outcomes: to know: application areas and basic applied aspects of machine learning; basic concepts and principles of work of artificial neural networks; problem statement and basic natural language processing methods; be able to: correctly formulate the tasks that arise in the practical activity, to solve them by means of machine learning methods; to analyze a specific problem in order to select the best method of machine learning for its solution; to carry out the analysis and synthesis of informative features; to analyze the work of machine learning methods to identify their strengths and weaknesses
Required prior and related subjects: Discrete Math Mathematical analysis Linear algebra Probability theory Mathematical statistics
Summary of the subject: Advances in Imaging: VR-AR, Machine Learning, and Self-Driving Cars - Examine emerging solutions, like machine learning, that are opening up new research and commercial opportunities in immediate and future applications, including VR-AR, self-driving cars, and others.
Assessment methods and criteria: Current control Laboratory work 40 points Examination control Written component of 60 points Oral component 0 points Total 100 points
Recommended books: Stephen Marsland. Machine Learning). Лінійна: An Alg). Лінійнаorithmic Perspective, 452 р., 2015. Ethem Alpaydin. Introduction To Machine Learning). Лінійна, 584 p., 2009. Tom M. Mitchell. Machine Learning). Лінійна [http://www.cs.cmu.edu/~tom/mlbook.html]