| ARTIFICIAL INTELLIGENCE ENGINEERING | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| AIN2001 | Principles of Artificial Intelligence | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Must Course |
| Course Level: | Bachelor |
| Mode of Delivery: | Hybrid |
| Course Coordinator : | Instructor MUSTAFA ÜMİT ÖNER |
| Course Objectives: | The course aims to present the fundamentals and techniques of Artificial Intelligence. |
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The students who have succeeded in this course; The students who have succeeded in this course will be able to; 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. |
| 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. |
| Week | Subject | Related Preparation | |
| 1) | A Review of AI Concepts Rational Agents | ||
| 2) | Solving Problems by searching - Search algorithms (Uninformed and Informed) | ||
| 3) | Solving Problems by searching - Constraint Satisfaction Problems | ||
| 4) | Games - Adversarial Search, Game theory | Assignment #1 | |
| 5) | Logical agents - Propositional logic, First Order Logic and inference | ||
| 6) | Planning | ||
| 7) | Probabilistic Reasoning - Basic probability concepts, Bayesian inference | Assignment #2 | |
| 8) | Probabilistic Reasoning - Naive Bayes models, Bayesian networks | ||
| 9) | Machine Learning - Supervised vs. unsupervised learning, Decision trees, Nearest neighbor classifiers, Support Vector Machines | Midterm Exam | |
| 10) | Neural Networks | Assignment #3 | |
| 11) | Deep Learning - Convolutional Neural Networks | ||
| 12) | Deep Learning | Assignment #4 | |
| 13) | Reinforcement Learning - Markov decision processes, Q-learning | ||
| 14) | AI, Ethics and Society | ||
| Course Notes: | Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010. Selected papers (an additional listing of literature will be provided based on the students’ projects) |
| References: | Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010. Selected papers (an additional listing of literature will be provided based on the students’ projects) |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 10 | % 10 |
| Homework Assignments | 4 | % 20 |
| Presentation | % 0 | |
| Project | % 0 | |
| Seminar | % 0 | |
| Midterms | 1 | % 20 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 50 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 0 | 0 | 0 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework Assignments | 4 | 10 | 40 |
| Quizzes | 10 | 1 | 10 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 1 | 22 | 22 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 1 | 26 | 26 |
| Total Workload | 140 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Have sufficient background in mathematics, science and artificial intelligence engineering. | 5 |
| 2) | Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions. | 5 |
| 3) | Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose. | 5 |
| 4) | Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction. | 5 |
| 5) | Select and use modern techniques and tools necessary for engineering applications. | 5 |
| 6) | Design and conduct experiments, collect data, and analyse and interpret results. | 5 |
| 7) | Work effectively both as an individual and as a multi-disciplinary team member. | |
| 8) | Access information via conducting literature research, using databases and other resources | |
| 9) | Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning. | |
| 10) | Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field. | |
| 11) | Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level. | |
| 12) | Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age. | |
| 13) | Have a sense of professional and ethical responsibility. | |
| 14) | Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices. |