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
MAT2008	Introduction to Optimization	Spring	3	0	3	6

Basic information

Language of instruction:	English
Type of course:	Must Course
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Prof. Dr. SÜREYYA AKYÜZ
Course Objectives:	The aim of this course is to teach students the basic concepts and methods of optimization, to focus on topics such as mathematical optimization models, linear programming and heuristic optimization, to explore different optimization techniques, to make applications regarding the use of optimization in artificial intelligence by addressing optimization with constraints, artificial intelligence and optimization issues.

Learning Outcomes

The students who have succeeded in this course;
Students who can successfully complete this course;
1) Can investigate unconstrained optimization methods in an optimization problem where first-order and second-order necessary and sufficient conditions are given.
2) Apply numerical methods such as steep descent method, trust region, Newton methods, which are unconstrained optimization methods, and learn their use in artificial intelligence.
3) Learns the first and second order optimality conditions for constrained optimization, can find the optimal solution by applying Karush Kuhn Tucker conditions.
4) Knows how to solve the simplex method, one of the Linear Programming methods, with graphical and Tableau methods, and can implement it with Python.
5) Knows how to apply heuristic optimization methods and can model them in artificial neural networks.

Course Content

Fundamentals of unconstrained optimization
Newton methods
Line Search methods
Trust Region methods
Quasi Newton methods
Nonlinear least squares problems
Conjugate Gradient methods
Theory of constrained optimization
Theory of linear programming, Simplex method
Introduction to Heuristic Optimization
Hill Climbing Algorithm
Genetic Algorithm
Simmulated Annealing Algorithm
Particle Swarm Optimization

Weekly Detailed Course Contents

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Sources

Course Notes / Textbooks:	1) J. Nocedal and S.J. Wright, Numerical Optimization, Springer, 2006 2) I. Griva, S. G. Nash and A. Sofer, Linear and nonlinear programming, 2nd edition, SIAM, Philadelphia, 2009
References:	1) J. Nocedal and S.J. Wright, Numerical Optimization, Springer, 2006 2) I. Griva, S. G. Nash and A. Sofer, Linear and nonlinear programming, 2nd edition, SIAM, Philadelphia, 2009

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	4	% 25
Midterms	1	% 35
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

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.	5
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
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.	3
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.	3
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.