Ders Planı

CLASS 1

Lesson plan /

Lesson Information

Course Credit
Course ECTS Credit
Teaching Language of Instruction	İngilizce
Level of Course	Bachelor's Degree, TYYÇ: Level 6, EQF-LLL: Level 6, QF-EHEA: First Cycle
Type of Course
Mode of Delivery	Face-to-face
Does the course require compulsory or optional work experience?
Course Coordinator	Prof. Dr. RAFET AKDENİZ
Instructor (s)
Course Assistant

Purpose and Content

The aim of the course	The course aims to present the fundamentals and techniques of Artificial Intelligence. Have the fundamental knowledge on principles of artificial intelligence, formulate a state space description of a problem and to develop an algorithm for the problem. Compare and evaluate the most common models for knowledge representation and planning. Implement some of the basic algorithms for supervised learning and unsupervised learning.Develop problem solving skills on various artificial intelligence problems and implement related applications.
Course Content	The first part of the course begins with an overview of intelligent agents and agent architectures. We then introduce basic search techniques for problem solving and planning. Adversarial search and the principals of game theory are given. Knowledge representation and logical formalisms using propositional and first order logic are explained. Planning in partial observable environments is introduced. In the second part, we first give a summary of probability theory for Artificial Intelligence applications. Then machine learning algorithms including supervised and unsupervised learning are discussed. Deep learning is briefly explained. We discuss the applications of AI including computer vision, robotics and NLP. Finally, we give the impacts of AI in society and ethics.

The aim of the course

The course aims to present the fundamentals and techniques of Artificial Intelligence. Have the fundamental knowledge on principles of artificial intelligence, formulate a state space description of a problem and to develop an algorithm for the problem. Compare and evaluate the most common models for knowledge representation and planning. Implement some of the basic algorithms for supervised learning and unsupervised learning.Develop problem solving skills on various artificial intelligence problems and implement related applications.

Course Content

The first part of the course begins with an overview of intelligent agents and agent architectures. We then introduce basic search techniques for problem solving and planning. Adversarial search and the principals of game theory are given. Knowledge representation and logical formalisms using propositional and first order logic are explained. Planning in partial observable environments is introduced. In the second part, we first give a summary of probability theory for Artificial Intelligence applications. Then machine learning algorithms including supervised and unsupervised learning are discussed. Deep learning is briefly explained. We discuss the applications of AI including computer vision, robotics and NLP. Finally, we give the impacts of AI in society and ethics.

Learning Outcomes

Adequate knowledge in mathematics, physical sciences and the computer engineering subjects of individual branches; the ability to implement theoretical and practical knowledge in the respective areas to modeling and solving computer engineering problems.
Ability to reflect the developments in Information and Communication Technologies to the applications of Information Technology.
Ability to identify, define, formulate and solve complex computer engineering problems; ability to select and implement the appropriate analysis and modeling methods in this respect.
Ability to design experiments for analysing computer engineering problems, to conduct experiments, to collect data, to analyse and interpret the results.
Ability to design a complex informtacis system, process, device or product to meet specific requirements under realistic restrictions and conditions; ability to implement the modern design methods for this purpose.
Ability to use, choose and develop modern techniques and means required for computer engineering applications; ability to use information technologies effectively.
Ability to work effectively in interdisciplinary and multi-disciplinary teams; ability to work individually..
Knowledge of applications in business life like project management, risk management and change management; awareness about entrepreneurship, innovativeness and sustainable development.
Awareness of the necessity of lifelong learning; ability to reach information, follow the latest developments in science and technology; ability to ensure self-renewal perpetually.
Ability to communicate efficiently both in oral and written ways in Turkish; knowledge of at least one foreign language; ability to effectively prepare reports and understand written reports, prepare design and production reports, effective presentation, giving and receiving clear and understandable instructions.
Knowledge on the effects of informatics applications on health, environment and safety in terms of universal and social aspects and information about the problems of the era; awareness on the legal results of informatics solutions.
Acting in line with ethical principles, sense of professional and ethical responsibility; knowledge on the standards of engineering applications.

Weekly Course Subjects

1	Solving Problems by searching - Search algorithms
2	Solving Problems by searching - Constraint Satisfaction Problems
3	Games - Adversarial Search, Game theory
4	Logical agents - Propositional logic, First Order Logic and inference
5	Reinforcement Learning - Markov decision processes, Q-learning
6	Neural Networks
7	Deep Learning - Convolutional Neural Networks
8	Midterm Exam
9	Reinforcement Learning - Markov decision processes, Q-learning
10	Ethics and Society
11	Probabilistic Reasoning - Naive Bayes models, Bayesian networks
12	Planning
13	Planning
14	General revision

Resources

1-Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
2- Ders Notları

CLASS 1

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CLASS 4

Lesson Information

Purpose and Content

Learning Outcomes

Weekly Course Subjects

Resources

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