Course Details

MATHEMATICAL MODELLING AND ALGORITHMIC THINKING

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
4	COMP206	MATHEMATICAL MODELLING AND ALGORITHMIC THINKING	3+2+0	4	5

Course Details

Language of Instruction	English
Level of Course Unit	Bachelor's Degree
Department / Program	COMPUTER ENGINEERING
Type of Program	Formal Education
Type of Course Unit	Compulsory
Course Delivery Method	Face To Face
Objectives of the Course	Improving Algorithmic Thinking abilities Improving Computational thinking abilities Understanding the problems and problem-solving steps Improving coding skills
Course Content	The main aim of this course is to improve Mathematical Modelling and Algorithmic Thinking methods for developing problem-solving abilities. There is always a relation between real-world problems and computer science-based solutions of them. This course will teach how to model a problem and think like a computer, especially for understanding the issues and finding proper ways to solve them. This class will explain how to make computers efficiently solve problems by discussing Computational thinking and designing steps for problem solving. Students will improve their knowledge of mathematics, algorithms, fundamentals of computer science (data structures, algorithms analysis, programming, etc.), and number theory.
Course Methods and Techniques	12 weeks lab exercises and theory class
Prerequisites and co-requisities	( COMP203 )
Course Coordinator	None
Name of Lecturers	Instructor Dr. Cavidan Yakupoğlu Karaağaç cavidan.yakupoglu@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Data Structures and Algorithms in Java, 6th edition, M. T. Goodrich, R. Tamassia, M. H. Goldwasser, Wiley, 2014.
	Data Structures and Algorithms in Java, 6th edition, M. T. Goodrich, R. Tamassia, M. H. Goldwasser, Wiley, 2014. and leetcode and hackerrank exercises

Course Category

Mathematics and Basic Sciences	%20
Engineering	%40
Engineering Design	%40
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
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı	1	% 20
Ödev	5	% 40
Laboratuar	12	% 15
Final examination	1	% 25
Total	19	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Yazılı Sınav	1	6	6
Ev Ödevi	5	15	75
Kısa Sınav	1	20	20
Yazılım Deneyimi	12	3	36
Final Sınavı	1	13	13
Total Work Load			Number of ECTS Credits 5 150

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

No	Learning Outcomes
1	Use algorithmic thinking principles to analyze real-world problems
2	Create solution models for real-world problems
3	Identify proper solution steps
4	Code the problem solutions with programming languages
5	Design different solutions to a given problem

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to algorithms and modelling in general
2	Complexity Analysis of Algorithms: The Master Method
3	Leetcode exercises-1
4	Leetcode exercises-2
5	Data structures wrap up of last semester and Maps
6	hash tables
7	Spring break
8	Midterm and active learning week activities
9	Avl Trees- Red-Black trees-2-4 Trees
10	Graphs, BFS and DFS algorithms
11	Graphs Algorithms- Shortest Path and Minimum Spanning Tree Algorithms
12	Graphs Algorithms- Shortest Path and Minimum Spanning Tree Algorithms
13	Dynamic programming
14	Linear programming

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=74943&lang=en