Introduction to Artificial Intelligence

Course Code:

CSE 462

Course Period:

Autumn

Course Type:

Area Elective

Credits:

3

Theoric:

3

Practice:

0

Laboratory Hour:

0

ECTS:

5

Course Language:

English

Course Coordinator:

Emin Erkan Korkmaz

Courses given by:

Emin Erkan Korkmaz

Course Objectives:

The aim of this course is to provide students the knowledge about the basic techniques and methodologies of artificial intelligence and abilities to apply artificial intelligence methods on practical problems.

Course Content:

Basic concepts and techniques of AI. Problem solving in AI, informed and uninformed search techniques, Local search techniques and simulated annealing. Meta-heuristic search methods. Introduction to Neural Networks. Game playing, Prolog overview, knowledge representation and reasoning.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Lab, 4: Case-study

Course Evaluation Methods:

A: Testing, B: Experiment, C: Homework, D: Project

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Course Learning Outcomes

Course Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
1) Knowledge about the basic methodologies in artificial intelligence.	1	1,2,3	A, C
2) Ability to use knowledge to formulate, and solve practical problems using artificial intelligence techniques.	2	1,2,3	A, C

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introductory terms, foundations, history and philosophy of AI	Textbook
2	Intelligent Agents	Textbook
3	Problem Solving and Introduction to Search Methods	Textbook
4	Uninformed Search Methodologies	Textbook
5	Heuristic Search	Textbook
6	Game Playing	Textbook
7	Meta-Heuristics	Textbook
8	Neural Networks	Textbook
9	Knowledge Based Agents	Textbook
10	First Order Logic	Textbook
11	Inference in First Order Logic	Textbook
12	Prolog and Logic Programming	Textbook
13	Prolog and Logic Programming	Textbook
14	Probabilistic Reasoning	Textbook

Recommended Sources

RECOMMENDED SOURCES
Textbook	Artificial Intelligence: A Modern Approach. Stuart Russell, Peter Norvig, Prentice Hall, Second Edition
Additional Resources

Material Sharing

MATERIAL SHARING
Documents
Assignments
Exams

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-terms	1	50
Assignment	4	50
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

Course’s Contribution to Program

COURSE’S CONTRIBUTION TO PROGRAM
No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.					X
2	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.				X
3	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose.				X
4	Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.				X
5	Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems.			X
6	Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.			X
7	Ability to communicate effectively both orally and in writing; knowledge of a minimum of one foreign language.
8	Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.			X
9	Awareness of professional and ethical responsibility.	X
10	Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
11	Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.		X

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Excluding the exam week: 13x Total course hours)	13	3	39
Hours for off-the-classroom study (Pre-study, practice)	14	2	28
Midterm examination	1	2	2
Homework	4	15	60
Final examination	1	3	3
Total Work Load			132
Total Work Load / 25 (h)			5.28
ECTS Credit of the Course			5

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Introduction to Artificial Intelligence

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