INTERNATIONAL FINANCE
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
CMP4501	Introduction to Artificial Intelligence and Expert Systems	Spring Fall	3	0	3	6

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 :	Dr. Öğr. Üyesi TEVFİK AYTEKİN
Recommended Optional Program Components:	None
Course Objectives:	The course introduces basics of artificial intelligence. Basic search techniques used for problem solving, fundamentals of knowledge representation and logical formalisms, basic learning algorithms, and fundamentals of expert systems will be introduced.

Learning Outcomes

The students who have succeeded in this course;
I. Be able to formulate a state space description of a problem
II. Be able to select and implement brute-force or heuristic algorithm for a problem.
III. Be able to implement minimax search with alpha-beta pruning.
IV. Be able to compare and evaluate the most common models for knowledge representation.
V. Be able to explain the operation of the resolution technique for theorem proving.
VI.Be able to explain the differences among supervised and unsupervised learning.
VII. Be able to explain the concepts of overfitting, underfitting, bias, and variance.
VIII. Be able to implement some of the basic algorithms for supervised learning and unsupervised learning.
IX. Be able to describe fundamentals of expert systems and evaluate them.

Course Content

Introduction to AI, state spaces and searching, heuristic functions and search, alpha-beta pruning, propositional and first-order predicate logic, propositional and first order inference, unification and resolution, linear regression, logistic regression, neural networks and backpropagation algorithm, Bayes’ rule and naive Bayes algorithm, clustering and k-means algorithm, fundementals of expert systems, software for expert systems.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to AI
2)	State spaces and searching.
3)	Heuristic functions and search
4)	Decisions in games, alpha-beta pruning.
5)	Propositional and first-order predicate logic
6)	Propositional and first order inference
7)	Unification and resolution
8)	Linear Regression
9)	Midterm
10)	Logistic Regression
11)	Neural networks and backpropagation algorithm.
12)	Bayes’s rule and naive Bayes algorithm.
13)	Clustering and k-means algorithm
14)	Fundementals of expert systems.
15)	Software for expert systems.

Sources

Course Notes / Textbooks:	Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, (3rd edition), 2009. Giarratano, J.C., Riley, G.D., Expert Systems: Principles and Programming, (4th edition), 2004.
References:	Yok - None

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	2	% 10
Project	1	% 20
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Project	4	20
Homework Assignments	10	20
Quizzes	2	8
Midterms	5	15
Final	5	20
Total Workload		125

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)	To correctly identify the problems and to be able to ask the correct questions	2
2)	To have the ability for problem solving and to utilize analytical approach in dealing with the problems of finance	1
3)	To understand and grasp the full details of theoretical arguments and counter arguments	2
4)	To be fully prepared for a graduate study in finance and to have lifelong learning awareness	2
5)	To be able to apply theoretical principles of finance to the realities of practical business life	1
6)	To develop solutions for managerial problems by understanding the requirements of international financial markets	2
7)	To think innovatively and creatively in complex situations	3
8)	To be able to make decisions both locally and internationally by knowing the effects of globalization on business and social life	2
9)	To have the competencies of the digital age and to use the necessary financial applications	2
10)	To be able to use at least one foreign language both for communication and academic purposes	1
11)	To understand the importance of business ethics and to take decisions by knowing the legal and ethical consequences of their activities in the academic world and business life	2
12)	To develop an objective criticism in business and academic life and having a perspective to self-criticize	2