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 upper and advanced level applications of mathematics to practical tasks. Problems examined from different perspective and wide range of mathematical tools are discussed as possible solution direction. Implementation outcomes are compared to theoretical predictions. Topics come from wide area including efficient computation, computer graphics and machine learning.

Course Content

Data Matrix and Numerical Attributes, Python Basics for Machine Learning and Data Analysis, Introduction to Kernel Methods, Kernel Principal Component Analysis, Clustering Techniques; KMeans Clustering, Clustering Techniques; Hierarchical Clustering, Decision Tree Classifier, Random Forest Classifier, Linear Discriminant Analysis, Multivariate Linear Regression, Multivariate Logistic Regression, Text Analysis, Kernel Support Vector Machines

Course Methods and Techniques


Prerequisites and corequisities

None

Course Coordinator

None

Name of Lecturers

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

Assistants

None

Work Placement(s)

No

Recommended or Required Reading
Resources

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









