BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP5103	Artificial Intelligence	Fall Spring	3	0	3	8

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:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Assoc. Prof. TEVFİK AYTEKİN
Course Lecturer(s):	Prof. Dr. NAFİZ ARICA
Recommended Optional Program Components:	None
Course Objectives:	The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems.

Learning Outcomes

The students who have succeeded in this course;
I. Be able to solve problems by applying a suitable search method.
II. Be able to implement minimax search and alpha-beta pruning in game playing.
III. Be able to use logical formalisms in modeling.
IV. Be able to apply supervised learning techniques to a given problem.
V. Be able to apply unsupervised learning techniques to a given problem.
VI. Be able to use the basic techniques in natural language processing.

Course Content

introduction; uninformed search strategies; informed (heuristic) search strategies; adversarial search; propositional logic; predicate logic; supervised learning techniques; unsupervised learning techniques; natural language processing.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction
2)	Uninformed Search Strategies
3)	Uninformed Search Strategies
4)	Informed (Heuristic) Search Strategies
5)	Informed (Heuristic) Search Strategies
6)	Adversarial Search
7)	Propositional Logic
8)	Predicate logic
9)	Supervised Learning Techniques
10)	Supervised Learning Teknileri
11)	Unsupervised Learning Techniques
12)	Unsupervised Learning Techniques
13)	Natural Language Processing
14)	Natural Language Processing
15)	Final Exam

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:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Presentation	1	% 10
Project	1	% 50
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 10
PERCENTAGE OF FINAL WORK		% 90
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	3	42
Project	1	42	42
Homework Assignments	7	8	56
Final	1	12	12
Total Workload			194

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 be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems.
2)	To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management.
3)	To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained.
4)	Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards.
5)	To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice.
6)	Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively.
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications.