Computer Programming

Course Code	Course Name	Year	Period	Semester	T+A+L	Credit	ECTS
03831198	Introduction to Artificial Intelligence	2	Autumn	3	2+1+0	5	5

Course Type	Elective
Course Cycle	Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5)
Course Language	Turkish
Methods and Techniques	Proje Tabanlı Öğrenme (PBL), Vaka Çalışmaları ve Gerçek Hayat Örnekleri
Mode of Delivery	Face to Face
Prerequisites	-
Coordinator	-
Instructor(s)	Lect. Abubakar MAYANJA
Instructor Assistant(s)	-

Name and Surname	Room	E-Mail Address	Internal	Meeting Hours
Lect. Abubakar MAYANJA	TSMYO-T213	[email protected]	7829	Wednesday 11:00 -12:00

Basic Vocational Courses	X
Specialization / Field Courses	X
Support Courses	X
Transferable Skills Courses
Humanities, Communication and Management Skills Courses

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Program Learning Outcomes	Level
P4	Effectively uses information technologies (software, programs, animations, etc.) related to her/his profession.	5
P5	Has the ability to independently evaluate professional problems and issues with an analytical and critical approach and propose solutions.	4
P11	Creates algorithms and data structures and performs mathematical calculations.	4
P12	Explains and applies web programming technologies.	1

Upon the successful completion of this course, students will be able to:
No	Learning Outcomes	Outcome Relationship	Measurement Method **
O1	Knows how to develop algorithms and creates a data structure suitable for the algorithm.	P.4.1	3,6,7
O2	Knows the basic elements of a computer.	P.1.1	1,6,7
O3	Knows how to use the internet and do research.	P.1.2	6,7
O4	Knows current techniques for data analysis.	P.3.1	1,3,6,7
** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7

Week	Topics
1	Introduction to AI 1.1 Definition and History of AI What is AI? Brief history of AI development Key milestones in AI, Types of AI Narrow vs. General AI Reactive machines, limited memory, theory of mind, and self-aware AI
2	Foundations of AI, Key Concepts Machine learning vs. traditional programming Data, algorithms, and models, Overview of Machine Learning: Supervised, unsupervised, and reinforcement learning Common algorithms: decision trees, neural networks, k-means clustering
3	AI Techniques: Search Algorithms Uninformed search (e.g., breadth-first, depth-first) Informed search (e.g., A* algorithm). Natural Language Processing (NLP): Introduction to NLP and its applications Text processing and sentiment analysis
4	Neural Networks and Deep Learning: Introduction to Neural Networks Structure of a neuron and neural networks Activation functions and training process. Deep Learning: Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs)
5	AI in Practice: Applications of AI AI in healthcare, finance, transportation, and entertainment Case studies of successful AI implementations: AI Tools and Frameworks Overview of popular AI frameworks (e.g., TensorFlow, PyTorch) Setting up a simple AI project
6	Ethical Considerations and Future of AI. Ethics in AI: Bias in AI algorithms Privacy concerns and data security AI and job displacement. The Future of AI: Trends in AI research and development Potential impacts of AI on society
7	Mid-Term exams
8	Hands-on Project. Project Overview: Define a project involving AI application (e.g., building a simple chatbot, image classifier). Project Development: Guidance on data collection, model selection, and evaluation
9	Hands-on Project. Project Overview: Define a project involving AI application (e.g., building a simple chatbot, image classifier). Project Development: Guidance on data collection, model selection, and evaluation
10	Hands-on Project. Project Overview: Define a project involving AI application (e.g., building a simple chatbot, image classifier). Project Development: Guidance on data collection, model selection, and evaluation
11	Course Review and Final Presentations: Review of Key Concepts: Recap major topics covered in the course. Final Presentations Students present their projects and findings
12	Course Review and Final Presentations: Review of Key Concepts: Recap major topics covered in the course. Final Presentations Students present their projects and findings
13	Course Review and Final Presentations: Review of Key Concepts: Recap major topics covered in the course. Final Presentations Students present their projects and findings
14	Final Exam

Resources	Prolog Programming for Artificial Intelligence – Ivan Bratko
	Prolog Programming for Artificial Intelligence – Ivan Bratko
	Prolog Programming for Artificial Intelligence – Ivan Bratko
	Artificial Intelligence: A Guide to Intelligent Systems" by Michael Negnevitsky

In-Term Studies	Quantity	Percentage
Attendance	-	-
Laboratory	-	-
Practice	-	-
Field Study	-	-
Course Specific Internship (If Any)	-	-
Homework	1	-
Presentation	-	-
Projects	-	-
Seminar	-	-
Quiz	-	-
Listening	-	-
Midterms	1	30 (%)
Final Exam	1	70 (%)
Total		100 (%)

	Quantity	Duration	Total Work Load
Course Week Number and Time	14	4	56
Out-of-Class Study Time (Pre-study, Library, Reinforcement)	14	3	42
Midterms	1	15	15
Quiz	0	0	0
Homework	0	0	0
Practice	14	1	14
Laboratory	14	1	14
Project	1	15	15
Workshop	0	0	0
Presentation/Seminar Preparation	0	0	0
Fieldwork	0	0	0
Final Exam	1	15	15
Other	0	0	0
Total Work Load:			171
Total Work Load / 30			5,70
Course ECTS Credits:			6

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Learning Outcomes	P1	P3	P4
O1	Knows the basic elements of a computer.	4	-	-
O2	Knows how to use the internet and do research.	4	-	-
O3	Knows current techniques for data analysis.	-	5	-
O4	Knows how to develop algorithms and creates a data structure suitable for the algorithm.	-	-	3