USEFUL INFORMATION, RESOURCES & SERVICES FOR STUDENTSUSEFUL LINKS AND DOCUMENTS ADITIONAL & SUPPORTING INFORMATION

SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
60619METOZ-YZM0135	Artificial Intelligence	3	Fall	2	2	3	5

Course Type :	Compulsory
Cycle:	Bachelor TQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Dr. Öğr. Üyesi NEDİM MUZOĞLU
Dersin Öğretim Eleman(lar)ı:
Dersin Kategorisi:	Programme Specific

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

Artificial Intelligence explains various problem solving techniques and introduces basic machine learning techniques such as supervised and unsupervised learning. Apart from that, it aims to give a solid understanding of basic machine learning problems. In addition, the course introduces current machine learning methods such as decision trees, linear regression, k-nearest neighbor, Bayesian classifiers, neural networks, logistic regression and classifier combinations.

Course Content:

Within the scope of this course, first of all, introduction to Artificial Intelligence, the history of artificial intelligence, its basics; Application areas of artificial intelligence, followed by intelligent agents (agents), logical agents, problem solving by searching, games and puzzles, Heuristic search from various search algorithms, Local search, Hostile search, then Genetic algorithms, Machine Learning Clustering Algorithms, K- Nearest Neighboring Algorithm, Decision Trees, Fuzzy Logic topics will be covered.
This course employs the project-based learning approach. In this respect aside from the conventional content the course has a project-based learning component. The project based- learning component aims realising one or more projects designed for learning purposes involving the development of certain intermediary and final deliverables in a step-by-step mannerby the students individually or in project teams. The evaluation of the project-based learning component involves grading the project deliverables and the project works by the instructor and/or a jury.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:



Knowledge *(Described as Theoritical and/or Factual Knowledge.)*
1) Understand the fundamental concepts of knowledge based reasoning.
2) Know the fundamental concepts of agents and applications of agent theory to different domains.
Skills *(Describe as Cognitive and/or Practical Skills.)*
1) Formulate an efficient problem space for a problem expressed in English by expressing that problem space in terms of states, operators, and initial state, and a description of goal state.
2) Select an appropriate search algorithm (i.e. brute-force, heuristic) for a problem, implement it, and characterize its time and space complexities.
3) Know (be able to explain the differences between and implement simple algorithms of) three main styles of learning: supervised, reinforcement and unsupervised.
4) Apply concepts of artificial intelligence to different domains including games and puzzles, expert systems, planning, learning, vision, robotics and natural language understanding.
5) Program small scale artificial intelligence applications using Prolog.
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.)*

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Introduction to artificial intelligence The history and foundations of artificial intelligence, intelligent agents
2)	Smart Factors (Agent), Logical Factors, Determination of Projects
3)	Solving problems by searching, beyond classical research
4)	Heuristic Search
5)	Local Search
7)	Project 1 st review
8)	Midterm Exam
9)	Genetic Algorithms
10)	Introduction to Machine Learning
11)	Supervised Learning: Classification Algorithms, Linear Regression, Decision Trees
12)	Submission of the first delivery of the project
12)	Supervised Learning: Artificial Neural Networks, k-Nearest Neighbor Algorithm, Learning Vector Quantization, Project 2.review
13)	Unsupervised Learning: Clustering Algorithms, K-Means Algorithm, Hierarchical Clustering,
14)	Fuzzy Logic
15)	Project Final Presentation

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Level of Contribution of the Course to PLOs

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Has sufficient knowledge in mathematics, science, computer science and software engineering; use theoretical and applied knowledge in these fields together to solve software engineering problems	5
2)	Uses and applies theoretical and applied sciences in the field of basic science subjects for the solution of software engineering problems.
3)	Analyzes software engineering applications, designs and develops models to meet specific requirements under realistic constraints and conditions. For this purpose, selects and uses appropriate methods, tools and technologies.
4)	Identify, define, formulate and solve complex Software Engineering problems; for this purpose select and apply appropriate analytical and modeling methods	5
5)	Selects and effectively uses modern techniques and tools and information technologies required for computer science and Software Engineering applications.	5
6)	Designs a complex computer and software based system, process, device or product to meet certain requirements under realistic constraints and conditions, including economics, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues; For this purpose, it applies modern design methods.	5
7)	Evaluates the norms and standards present in the works in which s/he takes responsibility in a critical point of view.
8)	Have the competencies required by the constantly developing field of Software Engineering and the global competitive environment.
9)	Communicates effectively in Turkish, both orally and in writing, has at least one foreign language knowledge at the level of European Language Portfolio B2	1

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course
Lectures
Problem Solving
Demonstration
Project Preparation
Individual and Group Work

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Presentation
Final Exam
Report Evaluation
Jury Evaluation

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Project	2	% 25.00
Midterms	1	% 25.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	2	28
Laboratory	0	0	0
Application	14	2	28
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	14	2	28
Presentations / Seminar	4	1	4
Project	4	10	40
Homework Assignments	0	0	0
Total Workload of Teaching & Learning Activities	-	-	128
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	3	2	6
Midterms	1	6	6
Semester Final Exam	1	12	12
Total Workload of Assesment & Evaluation Activities	-	-	24
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			152
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			5

Course Notes / Textbooks:	Artificial Intelligence:A Modern Approach ,Stuart Russell & Peter Norvig Prentice Hall 2010 Alpaydın, E. 2020. Introduction to Machine Learning 4rd Edition. The MIT Press. Cambridge, MA. ISBN 9780262358064.
References:	Artificial Intelligence:A Modern Approach ,Stuart Russell & Peter Norvig Prentice Hall 2010 Alpaydın, E. 2020. Introduction to Machine Learning 4rd Edition. The MIT Press. Cambridge, MA. ISBN 9780262358064.