ECONOMICS AND 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	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)	Build up a body of knowledge in mathematics and statistics, to use them, to understand how the mechanism of economy –both at micro and macro levels – works.	3
2)	Understand the common as well as distinctive characters of the markets, industries, market regulations and policies.	2
3)	Develop an awareness of different approaches to the economic events and why and how those approaches have been formed through the Economic History and understand the differences among those approaches by noticing at what extent they could explain the economic events.	1
4)	Analyze the interventions of politics to the economics and vice versa.	3
5)	Apply the economic analysis to everyday economic problems and evaluate the policy proposals for those problems by comparing opposite approaches.	2
6)	Understand current and new economic events and how the new approaches to the economics are formed and evaluating.	2
7)	Develop the communicative skills in order to explain the specific economic issues/events written, spoken and graphical form.	3
8)	Know how to formulate the economics problems and issues and define the solutions in a well-formed written form, which includes the hypothesis, literature, methodology and results / empirical evidence.	2
9)	Demonstrate the quantitative and qualitative capabilities and provide evidence for the hypotheses and economic arguments.	2
10)	Understand the information and changes related to the economy by using a foreign language and communicate with colleagues.	3