Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| INTRODUCTION to AI | - | Fall Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Selim AKYOKUŞ |
| Name of Lecturer(s) | Prof.Dr. Selim AKYOKUŞ |
| Assistant(s) | |
| Aim | The objective of this course is to introduce and teach the fundamentals of problems, theories, algorithms and applications of Artificial Intelligence (AI). AI is a very fast-growning field that focuses on building intelligent systems that will have a great impact on every aera of industry, economy, and social life. The topics include definition and history of AI, problem solving via search, game playing, knowledge representation, propositional logic, first-order predicate logic, logical and probabilistic reasoning, planning, uncertain knowledge and reasoning, machine learning (popular machine learning algorithms, deep learning, reinforcement learning, and genetic algorithms), natural language processing, deep learning for natural language processing, computer vision and robotics. |
| Course Content | This course contains; Introduction and Intelligent Agents,Problem Solving by Searching,Adversarial Search and Games,Constraint Satisfaction Problems,Logical Agents,First-Order Logic, Inference in First-Order Logic,Knowledge Representation, Automated Planning,Uncertain knowledge and reasoning,Exam week,Probabilistic Programming, Making Simple Decisions, Making Complex Decisions,Machine Learning,Deep Learning, Reinforcement Learning ,Natural Language Processing, Deep Learning for Natural Language Processing,Computer Vision, Robotics,Review and presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Students will have an in-depth understanding of core areas of AI. | 6, 9 | A, E, F |
| Students will learn and gain an understanding of various search methods, knowledge representation, uncertainty, reasoning, machine learning, natural language processing, computer vision and robotics. | 6, 9 | A, E, F |
| Students will be able to choose the appropriate algorithm for solving an AI problem. | 6, 9 | A, E, F |
| Students will be introduced to the current research in artificial intelligence and encouraged to define research problems and develop effective solutions. | 6, 9 | A, E, F |
| Teaching Methods: | 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction and Intelligent Agents | |
| 2 | Problem Solving by Searching | |
| 3 | Adversarial Search and Games | |
| 4 | Constraint Satisfaction Problems | |
| 5 | Logical Agents | |
| 6 | First-Order Logic, Inference in First-Order Logic | |
| 7 | Knowledge Representation, Automated Planning | |
| 8 | Uncertain knowledge and reasoning | |
| 9 | Exam week | |
| 10 | Probabilistic Programming, Making Simple Decisions, Making Complex Decisions | |
| 11 | Machine Learning | |
| 12 | Deep Learning, Reinforcement Learning | |
| 13 | Natural Language Processing, Deep Learning for Natural Language Processing | |
| 14 | Computer Vision, Robotics | |
| 15 | Review and presentations |
| Resources |
| Artificial Intelligence: A Modern Approach, 4th Edition, by Stuart Russell and Peter Norvig, Pearson Education, 2021. |
| - Speech and Language Processing by Jurafsky and Martin, 2021. - G. F. Luger, Artificial Intelligence, Addison-Wesley, 2002. - Lectures notes ve web resources in AI. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | 1. An ability to apply knowledge of mathematics, science, and engineering | X | |||||
| 2 | 2. An ability to identify, formulate, and solve engineering problems | X | |||||
| 3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | X | |||||
| 4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | X | |||||
| 5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | X | |||||
| 6 | 6. An ability to function on multidisciplinary teams | X | |||||
| 7 | 7. An ability to communicate effectively | X | |||||
| 8 | 8. A recognition of the need for, and an ability to engage in life-long learning | X | |||||
| 9 | 9. An understanding of professional and ethical responsibility | X | |||||
| 10 | 10. A knowledge of contemporary issues | X | |||||
| 11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 6 | 10 | 60 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 2 | 5 | 10 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 15 | 15 | |||
| General Exam | 1 | 25 | 25 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 152 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(152/30) | 5 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| INTRODUCTION to AI | - | Fall Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Selim AKYOKUŞ |
| Name of Lecturer(s) | Prof.Dr. Selim AKYOKUŞ |
| Assistant(s) | |
| Aim | The objective of this course is to introduce and teach the fundamentals of problems, theories, algorithms and applications of Artificial Intelligence (AI). AI is a very fast-growning field that focuses on building intelligent systems that will have a great impact on every aera of industry, economy, and social life. The topics include definition and history of AI, problem solving via search, game playing, knowledge representation, propositional logic, first-order predicate logic, logical and probabilistic reasoning, planning, uncertain knowledge and reasoning, machine learning (popular machine learning algorithms, deep learning, reinforcement learning, and genetic algorithms), natural language processing, deep learning for natural language processing, computer vision and robotics. |
| Course Content | This course contains; Introduction and Intelligent Agents,Problem Solving by Searching,Adversarial Search and Games,Constraint Satisfaction Problems,Logical Agents,First-Order Logic, Inference in First-Order Logic,Knowledge Representation, Automated Planning,Uncertain knowledge and reasoning,Exam week,Probabilistic Programming, Making Simple Decisions, Making Complex Decisions,Machine Learning,Deep Learning, Reinforcement Learning ,Natural Language Processing, Deep Learning for Natural Language Processing,Computer Vision, Robotics,Review and presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Students will have an in-depth understanding of core areas of AI. | 6, 9 | A, E, F |
| Students will learn and gain an understanding of various search methods, knowledge representation, uncertainty, reasoning, machine learning, natural language processing, computer vision and robotics. | 6, 9 | A, E, F |
| Students will be able to choose the appropriate algorithm for solving an AI problem. | 6, 9 | A, E, F |
| Students will be introduced to the current research in artificial intelligence and encouraged to define research problems and develop effective solutions. | 6, 9 | A, E, F |
| Teaching Methods: | 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction and Intelligent Agents | |
| 2 | Problem Solving by Searching | |
| 3 | Adversarial Search and Games | |
| 4 | Constraint Satisfaction Problems | |
| 5 | Logical Agents | |
| 6 | First-Order Logic, Inference in First-Order Logic | |
| 7 | Knowledge Representation, Automated Planning | |
| 8 | Uncertain knowledge and reasoning | |
| 9 | Exam week | |
| 10 | Probabilistic Programming, Making Simple Decisions, Making Complex Decisions | |
| 11 | Machine Learning | |
| 12 | Deep Learning, Reinforcement Learning | |
| 13 | Natural Language Processing, Deep Learning for Natural Language Processing | |
| 14 | Computer Vision, Robotics | |
| 15 | Review and presentations |
| Resources |
| Artificial Intelligence: A Modern Approach, 4th Edition, by Stuart Russell and Peter Norvig, Pearson Education, 2021. |
| - Speech and Language Processing by Jurafsky and Martin, 2021. - G. F. Luger, Artificial Intelligence, Addison-Wesley, 2002. - Lectures notes ve web resources in AI. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | 1. An ability to apply knowledge of mathematics, science, and engineering | X | |||||
| 2 | 2. An ability to identify, formulate, and solve engineering problems | X | |||||
| 3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | X | |||||
| 4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | X | |||||
| 5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | X | |||||
| 6 | 6. An ability to function on multidisciplinary teams | X | |||||
| 7 | 7. An ability to communicate effectively | X | |||||
| 8 | 8. A recognition of the need for, and an ability to engage in life-long learning | X | |||||
| 9 | 9. An understanding of professional and ethical responsibility | X | |||||
| 10 | 10. A knowledge of contemporary issues | X | |||||
| 11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |