Course Information
SemesterCourse Unit CodeCourse Unit TitleL+PCreditNumber of ECTS Credits

Course Details
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; K-Means 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 co-requisities 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

Course Category

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm exams 1 % 30
Quizzes 6 % 30
Homeworks 0 % 0
Other activities 0 % 0
Laboratory works 0 % 0
Projects 0 % 0
Final examination 1 % 40
% 100

ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Weekly Course Time 28 1 28
Outside Activities About Course (Attendance, Presentation, Midterm exam,Final exam, Quiz etc.) 8 6 48
Laboratory 28 1 28
Exams and Exam Preparations 8 10 80
Total Work Load   Number of ECTS Credits 6 184

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 The ability to understand and apply advance linear algebra tools for solving sophisticated problem
2 To model and solve practical problems with the assistance of linear algebra and algorithms
3 The ability to translate mathematical knowledge to software products
4 Solidify advanced mathematical knowledge via practical applications

Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Data Matrix and Numerical Attributes
2 Python Basics for Machine Learning and Data Analysis
3 Introduction to Kernel Methods
4 Kernel Principal Component Analysis
5 Clustering Techniques; K-Means Clustering
6 Clustering Techniques; Hierarchical Clustering
7 Decision Tree Classifier
8 Random Forest Classifier
9 Linear Discriminant Analysis
10 Multivariate Linear Regression
11 Multivariate Logistic Regression
12 Text Analysis
13 Kernel Support Vector Machines
14 Other Methods
15 Final
16 Final

Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14
All 4 4 3 2 3
C1 2 3 3 2
C2 2 3 2
C3 4 3 3 2
C4 3

Contribution: 0: Null 1:Slight 2:Moderate 3:Significant 4:Very Significant