General Information about the Course

Course Code:	IKU-Y-224
Course Title:	Introduction to Artificial Intelligence
Course Semester:	2. Semester / Spring
Course Credits:	Theoretical Practical Credit ECTS 1 0 1 1
Language of instruction:	TR
Prerequisite of the course:	No
Ders İş Deneyimini Gerektiriyor mu?:	No
Type of course:	Üniversite Seçmeli
Level of course:	Associate TR-NQF-HE:5. Master`s Degree QF-EHEA:Short Cycle EQF-LLL:5. Master`s Degree
Course Lecturer(s):	Lecturer Tayfun ERDOĞAN
Course Assistants:

Purpose and content of the course

Course Objectives:	To teach students what artificial intelligence is at an introductory level, what can (and cannot) be done with artificial intelligence, and what are the application areas of artificial intelligence methods.
Course Objective:	This course is designed to provide a foundation in the field of artificial intelligence (AI). The course covers symbolic AI, AI concepts, terminology, and application areas. The overall goal is to demystify these concepts and provide students with a basic understanding of the past, present, and future of AI. This course is a non-technical introduction, meaning no prior knowledge of programming or mathematics is required.
Mode of Delivery:	E-Learning

Learning Outcomes

Knowledge (Described as Theoritical and/or Factual Knowledge.)

1) At the end of this course, students will have learned what Artificial Intelligence, augmented and generative intelligence is; what the concepts, terminology and applications of artificial intelligence (machine learning, deep learning, neural networks, natural language processing, large language models) are and in what form; how artificial intelligence is used / can be used in daily life and various industrial sectors.

Skills (Describe as Cognitive and/or Practical Skills.)

Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

Course Topics

Week	Subject
Related Preparation	Pekiştirme
1)	Historical Development of Artificial Intelligence, Categories
2)	Artificial Intelligence and Augmented Intelligence, Generative Artificial Intelligence and Examples
3)	Types of AI, Artificial Intelligence in Everyday Life
4)	Artificial Intelligence Chatbots, Smart Assistants and Chatbots
5)	Applications of Artificial Intelligence in Different Industries
6)	Artificial Intelligence Concepts, Terminology and Application Areas: Machine Learning
7)	Artificial Intelligence Concepts, Terminology and Application Areas: Deep Learning
8)	Artificial Intelligence Concepts, Terminology and Application Areas: Neural Networks
9)	Artificial Intelligence Concepts, Terminology and Application Areas: Natural Language Processing (NLP)
10)	Artificial Intelligence Concepts, Terminology and Application Areas, Speech Recognision and Computer Vision
11)	Artificial Intelligence Concepts, Terminology and Application Areas: Large Language Models(LLP) , Integration of Artificial Intelligence into Daily Life
12)	Artificial Intelligence Concepts, Terminology and Application Areas: Cloud Computing, Edge Computing
13)	Autonomous Vehicles, Artificial Intelligence Agents
14)	Artificial Intelligence Concepts, Terminology and Application Areas, Internet of Things (IoT)
References: Introduction to Artificial Intelligence, Moumita Ghosh,Thirugnanam Arunachalam AI Artificial Intelligence an Introduction,Leitner Introduction to Artificial Intelligence,Min-Yuh Day, Ph.D, Professor https://learning.edx.org/course/course-v1:IBM https://campus.datacamp.com/courses/large-language-models-llms-concepts/introduction-to-large-language-models-llm?ex=1

Ders - Program Öğrenme Kazanım İlişkisi

No Effect	1 Lowest	2 Average	3 Highest

Ders Öğrenme Kazanımları	1
Program Outcomes
1) To have in-depth knowledge of information security principles and methods.
2) To have a comprehensive knowledge of cyber security threats and attack types.
3) To have knowledge about cryptography techniques and applications.
4) To have knowledge about information security management systems and standards.
5) To have knowledge about secure software development processes and methods.
6) Ability to analyze and evaluate information security risks.
7) To have practical knowledge on computer networks and communication protocols.
8) Ability to identify and close security vulnerabilities.
9) Ability to work independently and take responsibility in information security projects.
10) Ability to demonstrate teamwork and leadership skills.
11) Ability to adapt to new technologies and security trends.
12) To be able to follow scientific and technological developments in the field.
13) Ability to express and present technical information in an understandable way.
14) Written and verbal communication skills.
15) Collaboration and teamwork skills.
16) To be aware of legal and ethical responsibilities related to information security.
17) Ability to respond to information security incidents and crisis management skills.

Course Teaching, Learning Methods

Q & A
Case Problem Solving/ Drama- Role/ Case Management
Laboratory
Quantitative Problem Solving
Fieldwork
Group Study / Assignment
Individual Assignment
WEB-based Learning
Internship
Practice in Field
Project Preparation
Report Writing
Seminar
Supervision
Social Activity
Occupational Activity
Occupational Trip
Application (Modelling, Design, Model, Simulation, Experiment et.)
Reading
Thesis Preparation
Field Study
Student Club and Council Activities
Other
Logbook
Interview and Oral Conversation
Research
Watching a movie
Bibliography preparation
Oral, inscribed and visual knowledge production
Taking photographs
Sketching
Mapping and marking
Reading maps
Copying textures
Creating a library of materials
Presentation

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance		% 0
Laboratory		% 0
Application		% 0
Practice Exam		% 0
Quizzes		% 0
Homework Assignments		% 0
Presentation		% 0
Project		% 0
Special Course Internship (Work Placement)		% 0
Field Study		% 0
Article Critical		% 0
Article Writing		% 0
Module Group Study		% 0
Brainstorming		% 0
Role Playing + Dramatizing		% 0
Out of Class Study		% 0
Preliminary Work, Reinforcement		% 0
Application Repetition etc.		% 0
Homework (reading, writing, watching movies, etc.)		% 0
Project Preparation + Presentation		% 0
Report Preparation + Presentation		% 0
Presentation / Seminar Preparation + Presenting		% 0
Oral examination		% 0
Midterms	1	% 40
Final	1	% 60
Practical Final		% 0
Report Submission		% 0
Bütünleme		% 0
Bütünleme Pratik		% 0
Kanaat Notu		% 0
Committee		% 0
Yazma Ödev Dosyası		% 0
Portfolio		% 0
Take-Home Exam		% 0
Logbook		% 0
Participation		% 0
Discussion		% 0
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Calculation of Workload and ECTS Credits

Activities	Number of Activities	Workload
Course Hours	13	13
Laboratory
Application
Practice Exam
Special Course Internship (Work Placement)
Field Work
Study Hours Out of Class
Article Critical
Article Writing
Module Group Study
Brainstorming
Role Playing + Dramatizing
Out-of-Class Study (Pre-study, Reinforcement, Practice Review, etc.)
Homework (reading, writing, watching movies, etc.)
Project Preparation + Presentation
Report Preparation + Presentation
Presentation / Seminar Preparation + Presenting
Oral examination
Preparing for Midterm Exams	7	7
MIDTERM EXAM (Visa)	1	1
Preparing for the General Exam	13	13
GENERAL EXAM (Final)	1	1
Participation
Discussion
Portfolio
Take-Home Exam
Logbook
Total Workload		35
ECTS (30 saat = 1 AKTS )		1