COMPUTER ENGINEERING (DR) (ENGLISH)
Qualification Awarded	Program Süresi	Toplam Kredi (AKTS)	Öğretim Şekli	Yeterliliğin Düzeyi ve Öğrenme Alanı
	4	240	FULL TIME	TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 8 QF-EHEA:Third Cycle TR-NQF-HE, ISCED (1997-2013):

Ders Genel Tanıtım Bilgileri

Course Code:

3072002016

Ders İsmi:

Artificial intelligence

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Labs	Credit	ECTS
3	0	0	3	6

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Other Recommended Topics for the Course:

none

Type of course:

Department Elective

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi Recep DURANAY

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

The objective of this course is to provide students with a comprehensive understanding of the principles, techniques, and applications of artificial intelligence. Students will learn about intelligent agents, search algorithms, knowledge representation, machine learning, and problem-solving approaches. The course will also cover areas such as natural language processing, computer vision, and robotics, giving students the knowledge needed to develop AI-based solutions. Practical assignments and projects using AI tools and frameworks will also be part of the course.

Course Content:

This course covers topics such as intelligent agents, search algorithms, knowledge representation, machine learning models, reinforcement learning, natural language processing, and robotics. Students will also learn about ethical concerns and challenges in AI applications. By the end of the course, they will be able to develop intelligent systems that solve real-world problems and apply AI techniques to various domains such as healthcare, business, and automation.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
2 - Skills
Cognitive - Practical
1) Use machine learning models to analyze and solve real-world problems.
3 - Competences
Communication and Social Competence
Learning Competence
1) Understand the key concepts of artificial intelligence and intelligent agents.
Field Specific Competence
1) Apply search algorithms and problem-solving techniques to find solutions for complex tasks.
2) Implement natural language processing techniques to work with text and speech data.
3) Understand and apply ethical principles and address challenges in AI development and applications.
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Introduction to Artificial Intelligence History, definitions, applications, intelligent agents.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
2)	Problem Solving and Search Algorithms Uninformed search, informed search, search trees, A* algorithm.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
3)	Knowledge Representation and Reasoning Logic, semantic networks, frames, expert systems.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
4)	Machine Learning Basics Supervised learning, unsupervised learning, classification, regression.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
5)	Decision Trees and Random Forests ID3 algorithm, pruning, random forests, overfitting.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
6)	Neural Networks and Deep Learning Perceptrons, backpropagation, convolutional neural networks (CNNs).	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
7)	Reinforcement Learning Markov Decision Processes, Q-learning, policy iteration.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
8)	Midterm
9)	Natural Language Processing (NLP) Text preprocessing, tokenization, part-of-speech tagging, named entity recognition.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
10)	Computer Vision Image processing, edge detection, object recognition.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
11)	Robotics and AI Robot motion planning, reinforcement learning for robotics.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
12)	Ethics in Artificial Intelligence Bias, fairness, accountability, transparency.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
13)	AI in Healthcare Applications of AI in medical diagnosis, drug discovery, personalized medicine.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
14)	AI in Business and Finance AI applications in finance, customer service, predictive analytics.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
15)	Real-world AI applications.	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
16)	Final Exam

Sources

Course Notes / Textbooks:	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
References:	Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	4	5	3
Program Outcomes

Ders - Öğrenme Kazanımı İlişkisi

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

Program Outcomes

Level of Contribution

Öğrenme Etkinliği ve Öğretme Yöntemleri

Course
Problem Çözme

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Gözlem

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	2	% 60
Semester Final Exam	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	10	18	180
Midterms	2	2	4
Final	1	2	2
Total Workload			186