ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
SEN2211	Data Structures and Algorithms I	Spring	2	2	3	7

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Assist. Prof. ÖZGE YÜCEL KASAP
Course Lecturer(s):	Assist. Prof. BETÜL ERDOĞDU ŞAKAR RA MERVE ARITÜRK Prof. Dr. NAFİZ ARICA Assist. Prof. DUYGU ÇAKIR YENİDOĞAN RA SEVGİ CANPOLAT
Recommended Optional Program Components:	None
Course Objectives:	This is an introductory course on common data structures that are used in software engineering. After completing the course, the student will have knowledge of applying, implementing and analysis of basic data structures, including, lists, stacks and queues. Certain fundamental techniques, such as sorting, searching and recursion are also taught. The teaching methods of the course are lecture and practice.

Learning Outcomes

The students who have succeeded in this course;
1) Describe and apply basic object oriented programming principles.
2) Implement basic data structures such as linked lists, stacks and queues.
3) Analyze the complexity and efficiency of algorithms.
4) Choose and design data structures for writing efficient programs.
5) Implement recursive algorithms.
6) Describe and implement sorting algorithms on common data structures.
7) Describe and implement search algorithms on common data structures.

Course Content

The course content is composed of object oriented Java review, the complexity and efficiency of algorithms, introduction to list-stack-queue structures, implementing list-stack-queue structures, recursion, searching algorithms and sorting algorithms.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Data Structures and Algorithms Complexity Analysis
2)	Introduction to Linked Lists
3)	Doubly Linked Lists Ordered Linked Lists
4)
5)	Stacks
6)	Stacks for Algebraic Operations
7)	Queues
8)	Queues
9)	Data Structure Classes in Java
10)	Recursion
11)	Recursive Complexity
12)	Searching Algorithms
13)	Sorting Algorithms
14)	Sorting algorithms

Sources

Course Notes / Textbooks:	Data Structures & Problem Solving Using Java (Mark Allen Weiss) Data Structures and Algorithm Analysis in Java (Mark Allen Weiss) Data Structures and Abstractions with Java (Frank Carrano)
References:	Yok

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Laboratory	2	% 25
Quizzes	2	% 15
Midterms	1	% 20
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Laboratory	14	2	28
Homework Assignments	1	8	8
Quizzes	2	20	40
Midterms	1	30	30
Final	1	30	30
Total Workload			164

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Build up a body of knowledge in mathematics, science and Artificial Intelligence Engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems.
2)	Design complex Artificial Intelligence systems, platforms, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose.
3)	Identify, formulate, and solve complex Artificial Intelligence Engineering problems; select and apply proper modeling and analysis methods for this purpose.
4)	Devise, select, and use modern techniques and tools needed for solving complex problems in Artificial Intelligence Engineering practice; employ information technologies effectively.
5)	Design and conduct numerical or physical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Artificial Intelligence Engineering.
6)	Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. Write and understand reports, prepare design and production reports, deliver effective presentations, give and receive clear and understandable instructions.
7)	Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself.
8)	Develop an awareness of professional and ethical responsibility, and behave accordingly. Be informed about the standards used in Artificial Intelligence Engineering applications.
9)	Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
10)	Acquire knowledge about the effects of practices of Artificial Intelligence Engineering on health, environment, security in universal and social scope, and the contemporary problems of Artificial Intelligence Engineering; is aware of the legal consequences of Mechatronics engineering solutions.
11)	Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Artificial Intelligence-related problems.