Computer Programming (Evening Education) Preview
Associate	TR-NQF-HE: Level 5	QF-EHEA: Short Cycle	EQF-LLL: Level 5

Course Introduction and Application Information

Course Code:

MYO047

Course Name:

Applied Artificial Intelligence

Semester:

Spring

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

Associate

TR-NQF-HE:5. Master`s Degree

QF-EHEA:Short Cycle

EQF-LLL:5. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Öğr. Gör. AHMET SELİM ÖVER

Course Lecturer(s):

Öğr. Gör. Burak Ağgül

Course Assistants:

Course Objective and Content

Course Objectives:	To understand the basic concepts and applications of artificial intelligence, to determine the learning algorithms with the appropriate optimization algorithm to be used in the analysis of the problems, to interprete the results obtained helping problems.
Course Content:	Introduction to artificial intelligence and basic concepts, problem analysis and solution, learning, different artificial intelligence algorithms, optimization algorithms.

Learning Outcomes

The students who have succeeded in this course;
1) To comprehend the basics of artificial neural network structures.
2) To comprehend the statistical learning.
3) To apply the difference machine learning algorithms.
4) To write a programs using artificial intelligence algorithms.
5) To understand the optimization algorithms required for machine learning.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction to Artifical Intelligence
2)	Introduction to Artifical Intelligence
3)	Statistical Learning
4)	Machine Learning
5)	Deep Learning
6)	Random Forest
7)	Support Vector Machines
8)	Midterm Exam
9)	k-En Yakın Komşu
10)	Recurrent Neural Networks
11)	Bidirectional Long Short-Term Memory
12)	Convolutional Neural Network
13)	Decision Trees
14)	Natural Language Processing
15)	Final

Sources

Course Notes / Textbooks:	1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer. 2.Wolfgang Ertel, Introduction to Artificial Intelligence, Second Edition, Springer. 3.Charu C. Aggarwal, Neural Networks and Deep Learning, Springer.
References:	1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer. 2.Wolfgang Ertel, Introduction to Artificial Intelligence, Second Edition, Springer. 3.Charu C. Aggarwal, Neural Networks and Deep Learning, Springer.

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) He gains the ability of problem solving and analytical thinking skills.
2) He learns the fundamentals of computer programming, hardware and software and the basic computer concepts.
3) He develops algorithms according to the problems, gains the ability to distinguish the appropriate ones from the fundamental algorithms for the problem.
4) He understands object-oriented programming concept and web programming.
5) He learns radix systems, fundamental electronics and computer hardware knowledge.
6) He gains mobile programming skills and develops applications for mobile platforms.
7) He designs and codes databases.
8) He learns to program and use computer networks, open source operating systems.
9) He uses the English language effectively.
10) He learns to use appropriate data structures according to programming requirements.
11) He develops software individually or as a team.
12) He follows developments in the field, high technology tools / applications.
13) He gains awareness of professional and ethical responsibility and has an awareness of professional ethics.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	He gains the ability of problem solving and analytical thinking skills.	3
2)	He learns the fundamentals of computer programming, hardware and software and the basic computer concepts.	3
3)	He develops algorithms according to the problems, gains the ability to distinguish the appropriate ones from the fundamental algorithms for the problem.	3
4)	He understands object-oriented programming concept and web programming.	3
5)	He learns radix systems, fundamental electronics and computer hardware knowledge.	3
6)	He gains mobile programming skills and develops applications for mobile platforms.	3
7)	He designs and codes databases.	3
8)	He learns to program and use computer networks, open source operating systems.	3
9)	He uses the English language effectively.	3
10)	He learns to use appropriate data structures according to programming requirements.	3
11)	He develops software individually or as a team.	3
12)	He follows developments in the field, high technology tools / applications.	3
13)	He gains awareness of professional and ethical responsibility and has an awareness of professional ethics.	3

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Spent for the Activity Itself	Completing the Activity Requirements	Workload
Course Hours	2	10	1		22
Midterms	1	25	1		26
Final	1	30	1		31
Total Workload					79