Course Information
SemesterCourse Unit CodeCourse Unit TitleL+PCreditNumber of ECTS Credits
6MATH338MATHEMATICS FOR MACHINE LEARNING3+036

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 This course aims to reinforce the mathematical knowledge used in machine learning with computer applications in Python.
Course Content Computer implementations of linear algebra, analytic geometry, matrix decomposition, vector calculus, probability, optimization, data analysis, regression analysis and dimension reduction
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator Instructor Dr. Barış Çiçek bariscicek@iyte.edu.tr
Dr.Öğr.Üyesi Haydar GÖRAL haydargoral@iyte.edu.tr
Name of Lecturers Dr.Öğr.Üyesi HAYDAR GÖRAL
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Mathematics for Machine Learning, M. P. Deisenroth, A. A. Faisal, C. S. Ong, 2019
Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!, A. Saha, 2015

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
Total
8
% 100

ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Weekly Course Time 14 3 42
Outside Activities About Course (Attendance, Presentation, Midterm exam,Final exam, Quiz etc.) 6 8 48
Exams and Exam Preparations 8 12 96
Total Work Load   Number of ECTS Credits 6 186

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 to be able to do computer applications of linear algebra
2 to be able to do computer applications of vector calculus
3 to be able to implement statistics and probability with python
4 to have the knowledge about mathematical optimization
5 to apply mathematics to machine learning problems using computer tools


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introducing the Course and Computer tools
2 Basic Concepts of Linear Algebra and its computer implementations in Python
3 Linear mappings and affine transformations
4 Norms, angles, orthogonality and orthogonal projection in Python
5 Matrix Decompositions and its computer implementations
6 Computing eigenvalues, eigenvectors, diagonalization of matrices and singular value decomposition in Python
7 Vector Calculus and its computer implementations in Python
8 Construction of a probability space, Discrete and continuous probabilities, and Bayes’ theorem with computer applications
9 Summary of statistics and Gaussian distribution in Python
10 Unconstrained and constrained optimization with applications
11 Convex optimization with applications
12 Data, features and model selection
13 Simple linear regression and Maximum likelihood estimation with computer applications
14 Dimensionality Reduction with Principal Component Analysis in Python
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 4 2 3 3
C1
C2
C3 3
C4 3
C5 3

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


https://obs.iyte.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=227344&lang=en