Course Detail

Course Description Course Outline Course Contribution to Program Qualifications Assessment Methods ECTS-Workload Detail Informations of the Course Numerical Data

Course Description

Course	Code	Semester	T+P (Hour)	Credit	ECTS
ARTIFICIAL INTELLIGENCE	BPR2214994	Spring Semester	3+0	3	5

Course Program

Prerequisites Courses
Recommended Elective Courses

Language of Course	Turkish
Course Level	Short Cycle (Associate's Degree)
Course Type	Elective
Course Coordinator	Lect. Beyza KOYULMUŞ
Name of Lecturer(s)	Lect. Beyza KOYULMUŞ
Assistant(s)
Aim	The aim of this course is to introduce and teach the fundamentals of Artificial Intelligence applications.
Course Content	This course contains; Introduction to Artificial Intelligence,Philosophy and History of Artificial Intelligence,Basic Concepts,Problem Solving with Artificial Intelligence,Machine Learning,Unsupervised, Supervised and Reinforcement Learning,Big Data and Computing Technology,Intelligent Agents,Deep Learning,Neural Networks,Natural Language Processing,Computer Vision,Predictive models and sample applications,The Future of Artificial Intelligence.

Dersin Öğrenme Kazanımları	Teaching Methods	Assessment Methods
Knows the concepts of Artificial Intelligence.	10, 16, 9	A, E, H
Knows the types of machine learning.	10, 16, 9	A, E
Knows the application areas of machine learning.	10, 16, 9	A, E, F
Knows the concepts of big data and computing technology.	16, 23, 9	A, E, F, G
Conducts current research in the field of artificial intelligence	16, 9	A, E, G
Understands the basics of artificial intelligence	10, 16, 9	A, E

Teaching Methods:	10: Discussion Method, 16: Question - Answer Technique, 23: Concept Map Technique, 9: Lecture Method
Assessment Methods:	A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz, H: Performance Task

Course Outline

Order	Subjects	Preliminary Work
1	Introduction to Artificial Intelligence
2	Philosophy and History of Artificial Intelligence
3	Basic Concepts
4	Problem Solving with Artificial Intelligence
5	Machine Learning
6	Unsupervised, Supervised and Reinforcement Learning
7	Big Data and Computing Technology
8	Intelligent Agents
9	Deep Learning
10	Neural Networks
11	Natural Language Processing
12	Computer Vision
13	Predictive models and sample applications
14	The Future of Artificial Intelligence

Resources

Course Contribution to Program Qualifications

Course Contribution to Program Qualifications
No	Program Qualification	Contribution Level
No	Program Qualification	1	2	3	4	5
1	Has the background in algorithms, programming, and application development in software engineering projects; and has the ability to use them together in business.				X
2	Chooses and uses the proper solution methods and special techniques for programming purpose.			X
3	Uses modern techniques and tools for programming applications.				X
4	Works effectively individually and in teams.			X
5	Implements and follows test cases of developed software and applications.				X
6	Has the awareness in workplace practices, worker health, environmental and workplace safety, professional and ethical responsibility, and legal issues about programming practices.		X
7	Reaches information, and surveys resources for this purpose.				X
8	Aware of the necessity of life-long learning; follows technological advances and renews him/herself.				X
9	Communicates, oral and written, effectively using modern tools.		X
10	Aware of universal and social effects of software solutions and practices; develops new software tools for solving universal problems and social advance.					X
11	Keeps attention in clean and readable code design.		X
12	Considers and follows user centered design principles.	X

Assessment Methods

Contribution Level	Absolute Evaluation
Rate of Midterm Exam to Success		40
Rate of Final Exam to Success		60
Total		100

ECTS / Workload Table
Activities				Number of	Duration(Hour)	Total Workload(Hour)
Course Hours				0	0	0
Guided Problem Solving				0	0	0
Resolution of Homework Problems and Submission as a Report				0	0	0
Term Project				0	0	0
Presentation of Project / Seminar				0	0	0
Quiz				0	0	0
Midterm Exam				0	0	0
General Exam				0	0	0
Performance Task, Maintenance Plan				0	0	0
Total Workload(Hour)						0
*Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30=(0/30)**						0
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.

Detail Informations of the Course

Course Description

Course	Code	Semester	T+P (Hour)	Credit	ECTS
ARTIFICIAL INTELLIGENCE	BPR2214994	Spring Semester	3+0	3	5

Course Program

Prerequisites Courses
Recommended Elective Courses

Language of Course	Turkish
Course Level	Short Cycle (Associate's Degree)
Course Type	Elective
Course Coordinator	Lect. Beyza KOYULMUŞ
Name of Lecturer(s)	Lect. Beyza KOYULMUŞ
Assistant(s)
Aim	The aim of this course is to introduce and teach the fundamentals of Artificial Intelligence applications.
Course Content	This course contains; Introduction to Artificial Intelligence,Philosophy and History of Artificial Intelligence,Basic Concepts,Problem Solving with Artificial Intelligence,Machine Learning,Unsupervised, Supervised and Reinforcement Learning,Big Data and Computing Technology,Intelligent Agents,Deep Learning,Neural Networks,Natural Language Processing,Computer Vision,Predictive models and sample applications,The Future of Artificial Intelligence.

Dersin Öğrenme Kazanımları	Teaching Methods	Assessment Methods
Knows the concepts of Artificial Intelligence.	10, 16, 9	A, E, H
Knows the types of machine learning.	10, 16, 9	A, E
Knows the application areas of machine learning.	10, 16, 9	A, E, F
Knows the concepts of big data and computing technology.	16, 23, 9	A, E, F, G
Conducts current research in the field of artificial intelligence	16, 9	A, E, G
Understands the basics of artificial intelligence	10, 16, 9	A, E

Teaching Methods:	10: Discussion Method, 16: Question - Answer Technique, 23: Concept Map Technique, 9: Lecture Method
Assessment Methods:	A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz, H: Performance Task

Course Outline

Order	Subjects	Preliminary Work
1	Introduction to Artificial Intelligence
2	Philosophy and History of Artificial Intelligence
3	Basic Concepts
4	Problem Solving with Artificial Intelligence
5	Machine Learning
6	Unsupervised, Supervised and Reinforcement Learning
7	Big Data and Computing Technology
8	Intelligent Agents
9	Deep Learning
10	Neural Networks
11	Natural Language Processing
12	Computer Vision
13	Predictive models and sample applications
14	The Future of Artificial Intelligence

Resources

Course Contribution to Program Qualifications

Course Contribution to Program Qualifications
No	Program Qualification	Contribution Level
No	Program Qualification	1	2	3	4	5
1	Has the background in algorithms, programming, and application development in software engineering projects; and has the ability to use them together in business.				X
2	Chooses and uses the proper solution methods and special techniques for programming purpose.			X
3	Uses modern techniques and tools for programming applications.				X
4	Works effectively individually and in teams.			X
5	Implements and follows test cases of developed software and applications.				X
6	Has the awareness in workplace practices, worker health, environmental and workplace safety, professional and ethical responsibility, and legal issues about programming practices.		X
7	Reaches information, and surveys resources for this purpose.				X
8	Aware of the necessity of life-long learning; follows technological advances and renews him/herself.				X
9	Communicates, oral and written, effectively using modern tools.		X
10	Aware of universal and social effects of software solutions and practices; develops new software tools for solving universal problems and social advance.					X
11	Keeps attention in clean and readable code design.		X
12	Considers and follows user centered design principles.	X

Assessment Methods

Contribution Level	Absolute Evaluation
Rate of Midterm Exam to Success		40
Rate of Final Exam to Success		60
Total		100

Numerical Data

Ekleme Tarihi: 05/11/2023 - 20:23Son Güncelleme Tarihi: 05/11/2023 - 20:25