Language of Instruction

English

Level of Course Unit

First Cycle

Department / Program

MATHEMATICS

Mode of Delivery

Face to Face

Type of Course Unit

Elective

Objectives of the Course

The course covers lower intermediate level applications of mathematics in various areas. Problems are formulated then various models and appropriate mathematical tools are applied to solve the problem. Hands on implementation is crucial part. Topics come from wide area including efficient computation, computer graphics and machine learning.

Course Content

Introduction to Data analysis, Programming tools for Machine Learning and Data Analysis, Principal Component Analysis, Singular Value Decomposition and its applications, Bivariate Linear Regression, Binary Logistic Regression, Stochastic Models: Markov Chains, Page Ranking Algorithm, Probabilistic Classification: Naïve Bayes Classifier, Testing Performance and Accuracy of the Computational Models, KNearest Neighbor, Support vector Machines

Course Methods and Techniques


Prerequisites and corequisities

None

Course Coordinator

None

Name of Lecturers

Instructor Dr. Barış ÇİÇEK Associate Prof.Dr. Berkant USTAOĞLU

Assistants

None

Work Placement(s)

No

Recommended or Required Reading
Resources

Slavik V. Jablan, Theory of Symmetry and Ornament, Mathematical Institute, 1995 Christopher M. Bishop  Pattern Recognition and Machine Learning, Springer, 2011. M. J. ZAKI, M. Wagner. Data Mining and Machine Learning, Fundamental Concepts and algorithms, Cambridge University Press, 2020









