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 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, K-Nearest Neighbor, Support vector Machines
Course Methods and Techniques
Prerequisites and co-requisities 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

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 linear algebra tools for solving 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 mathematical knowledge via practical applications

Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Data Analysis
2 Programming tools for Data Analysis and Machine Learning
3 Principal Component Analysis (PCA)
4 Singular Value Decomposition (SVD)
5 Applications of Singular Value Decomposition
6 Bivariate Linear Regressions
7 Binary Logistic Regression
8 Stochastic Models: Markov Chains
9 Page Rank Algorithm
10 Probabilistic Classification: Naïve Bayes Classifier
11 Testing Performance and Accuracy of Models
12 K-Nearest Neighbors (KNN)
13 Support Vector Machines (SVMs)
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