Course Details

Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BENG631VERİ ANALİZİNİN MATEMATİKSEL TEMELLERİ3+0+037,514.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Doctorate's Degree
Department / Program BIOENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course This course aims to introduce some of the fundamental machine learning problems with their connection with basic mathematical concepts.

- Providing fundamental knowledge of mathematical concepts in linear algebra, analytical geometry and calculus and skills to manipulate them.

- Introducing fundamental machine learning problems.

- Building a bridge between basic mathematics and complex machine learning problems.
Course Content In this series of lectures, we explore the deep link between mathematics and fundamental machine learning concepts. Firstly, we introduce/highlight some basic mathematical tools and then employ these concepts to present and solve some well-known machine learning problems. By the end of the semester, the students will be able to demonstrate an understanding of fundamental machine learning problems as well as their presentation in mathematical language
Course Methods and Techniques Face to face LEcture
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Instructor Dr. Uğur Kayaş https://avesis.agu.edu.tr/ugur.kayas ugur.kayas@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Convex Optimization, Cambridge University Press, by Stephen Boyd and Lieven Vandenberghe; Pattern recognition and machine learning. Springer, by Christopher M Bishop
Course Notes Text Book: Mathematics for Machine Learning, Cambridge University Press, by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
Documents https://aguedutr-my.sharepoint.com/:f:/g/personal/ugur_kayas_agu_edu_tr/EgaEXzMLoP9Ikf8FlGEJFr4Bn4IbWM9TyQO_a1NkxiWv1A?e=iISeoO
Assignments https://aguedutr-my.sharepoint.com/:f:/g/personal/ugur_kayas_agu_edu_tr/EgaEXzMLoP9Ikf8FlGEJFr4Bn4IbWM9TyQO_a1NkxiWv1A?e=iISeoO
Exams https://aguedutr-my.sharepoint.com/:f:/g/personal/ugur_kayas_agu_edu_tr/EgaEXzMLoP9Ikf8FlGEJFr4Bn4IbWM9TyQO_a1NkxiWv1A?e=iISeoO

Course Category
Mathematics and Basic Sciences %75
Engineering %25
Engineering Design %0
Social Sciences %0
Education %0
Science %0
Health %0
Field %0

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
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı 1 % 75
Sunum/Seminer 1 % 25
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Ev Ödevi 6 8 48
Sunum 1 10 10
Kişisel Çalışma 2 14 28
Teslim 1 90 90
Derse Devam 3 14 42
Total Work Load   Number of ECTS Credits 7,5 218

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 To develop proficiency in foundational mathematical concepts?including?linear algebra, analytical geometry, and calculus.
2 To understand basics of probability.
3 To introduce and examine optimization principles.
4 To apply mathematical techniques, such as linear algebraic transformations and calculus-based optimization methods, to analyze and solve?basic?machine learning problems.
5 To study real world problems and solve them with machine learning.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Fundamental Linear Algebra for Machine Learning: Matrices, Solving Systems of Linear Equations, Vector Spaces , https://aguedutr-my.sharepoint.com/:f:/g/personal/ugur_kayas_agu_edu_tr/Er_MG2SAdK9DnImuDJFzIcEBpRF4uq0GnnxtRfp01yMz_w?e=m6IK5S

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 5 5 5 5 5 5 5 5 5 5
C1 5 2 5 4 2 2 5 2 2 1 1 2
C2 5 2 5 4 2 2 5 2 2 1 1 2
C3 5 2 5 3 2 2 5 2 2 2 2 1
C4 5 3 5 4 2 2 5 2 2 2 1 2
C5 5 3 5 5 2 2 5 4 5 3 5 3

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77617&lang=en