Infopaket

Image Analysis Computer Vision

ННД.17.

Обов’язкова дисципліна для ОП

Second

2021/2022

3 Semester

5

1. Know the mathematical formulation of image processing problems, approaches to solving computer vision problems, tools of machine learning methods, basic concepts and principles of artificial neural networks. 2. Be able to analyze the problem of image processing and recognition to choose the best method to solve it. Be able to formulate professional tasks in the language of machine learning and on the basis of formulations of basic tasks of computer vision.

1. Know: basic disciplines - "Mathematical Analysis", "Linear Algebra and Geometry", "Probability Theory and Mathematical Statistics", "Programming", "Computational Geometry and Computer Graphics", "Algorithms and Data Structures", "Modern methods of machine learning "," Current issues of "Data Mining". 2. Be able to: develop, analyze and apply algorithms and software to solve application problems using modern methods of program development.

This educational program is a basic discipline of leading domestic and foreign universities specializing in information and computer technology, and, in particular, in the field of artificial intelligence. The purpose and objectives of the discipline is to get acquainted with one of the main scientific areas in the field of artificial intelligence and mastering the technology of solving a wide range of engineering and scientific problems (including image recognition, computer vision, intelligent control) using modern mathematical methods, approaches and algorithms and, in particular, machine learning methods.

6. Richard Szeliski. Computer Vision: Algorithms and Applications, Springer, 2010 7.Stephen Marsland. Machine Learning: An Algorithmic Perspective, 452 р., 2015. 8.Christopher M Bishop. Pattern recognition. Machine Learning, 128 p., 2006. 9.Ethem Alpaydin. Introduction To Machine Learning, 584 p., 2009. 10.Tom M. Mitchell. Machine Learning [http://www.cs.cmu.edu/~tom/mlbook.html] 11.Yaser S. Abu-Mostafa. Learning from data, 215 p., 2017 12.Alex Smola. Introduction to Machine Learning, 234 p., 2008. 13.Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction, 764 p., 2008.

Lecture, laboratory work, individual work.

Control work. Defense of laboratory work, exam.

Ukrainian language