MACHINE LEARNING AND PATTERN RECOGNITION

This course gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as ensemble methods, support vector machines, and Bayesian networks.

The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work.

Studying algorithms developed based on the paradigms of machine learning.

Analytical study of the advantages and disadvantages of learning-based algorithms automatic versus traditional algorithms in solving problems.

Training to address problems of high complexity from the perspective of learning-based algorithms