Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
1	ECE518	FUNDAMENTALS OF BIG DATA ANALYTICS	3+0+0	3	7,5	14.05.2025

Course Details

Language of Instruction	English
Level of Course Unit	Doctorate's Degree
Department / Program	ELECTRICAL AND COMPUTER ENGINEERING
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Gain an understanding of mathematical background of data mining Learn the techniques used for solving problems involving very large datasets Gain practice by completing programming assignments Apply the concepts to a real problem by completing a course project
Course Content	This course provides an introduction to big data analytics. It covers fundamental mathematical background of data mining and machine learning applications. The course also provides applications of graph mining tasks such as PageRank, etc. Methods will be implemented by a software and applied on various machine learning and data mining problems
Course Methods and Techniques	Lectures, Online lessons, Problem-solving and assignments, Project and presentation
Prerequisites and co-requisities	None
Course Coordinator	Associate Prof.Dr. Zafer Aydın zafer.aydin@agu.edu.tr
Name of Lecturers	Asist Prof.Dr. Mehmet Gökhan Bakal gokhan.bakal@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources
Course Notes	1. Applied Numerical Algebra by J.W Demmel 2. Numerical Optimization by Jorge Nocedal 3. Iterative Methods by Yousef Saad 3rd edition

Course Category

Mathematics and Basic Sciences	%40
Engineering	%10
Engineering Design	%50
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

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Yazılı Sınav	1	20	20
Ev Ödevi	10	7	70
Sunum için Hazırlık	1	4	4
Proje	1	30	30
Kısa Sınav	5	1	5
Okuma	14	1	14
Araştırma	1	5	5
Yüz Yüze Ders	14	3	42
Asenkron Ders	7	1	7
Final Sınavı	1	30	30
Total Work Load			Number of ECTS Credits 7,5 227

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Explain the mathematical principles and algorithms of Data Mining and Machine Learning models
2	Solve a machine learning problem using appropriate applied numerical linear algebra and numerical optimization methods
3	Implement big data analysis methods using a programming tool
4	Apply a big data analysis method to a real-world problem

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to numerical linear algebra, optimization problems, linear least squares problems
2	Tikhonov regularization and ridge regression, Lasso equations with pivoted QR decomposition
3	Fast solutions of large linear systems
4	PCA, matrix factorization
5	Basic Krylov subspaces
6	Randomized numerical linear algebra
7	Eigenvector computation, foundations of PageRank and RWR algorithms
8	Fundamentals of network analysis, generation of random graphs
9	Unsupervised graph learning, clustering and community detection
10	Both supervised and unsupervised learning
11	Learning node representations as vectors
12	Pattern discovery in graphs, triadic closures, simplex structures, etc.
13	Recommender systems
14	Final exam and project presentations

Contribution of Learning Outcomes to Programme Outcomes

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

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77860&lang=en