Information Package / Course Catalogue
| Electrical and Electronics Engineering | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
ECTS Course Information Package
| School/Faculty/Institute | Faculty of Engineering | |||||
| Course Code | COMP 450 | |||||
| Course Title in English | Artificial Intelligence | |||||
| Course Title in Turkish | Yapay Zeka | |||||
| Language of Instruction | EN | |||||
| Type of Course | Exercise,Flipped Classroom,Lecture | |||||
| Level of Course | Introductory | |||||
| Semester | Fall | |||||
| Contact Hours per Week |
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| Estimated Student Workload | 156 hours per semester | |||||
| Number of Credits | 6 ECTS | |||||
| Grading Mode | Standard Letter Grade | |||||
| Pre-requisites | None | |||||
| Co-requisites | None | |||||
| Expected Prior Knowledge | Basic mathematics knowledge | |||||
| Registration Restrictions | Only Undergraduate Students | |||||
| Overall Educational Objective | To learn the fundamental concepts of Artificial Intelligence and to become familiar with basic aspects of intelligent agents, knowledge representation, learning, and sensing. | |||||
| Course Description | This course provides a comprehensive introduction to some fundamental aspects of Artificial Intelligence. The following topics are covered: Introduction, Intelligent agents, Search algorithms, A*search and heuristics, constraint satisfaction problems, Game trees, Knowldege representation, Learning: reinforcement learning, Decision trees, evolutionary methods, Artificial Neural Networks, Perceptrons, Deep Learning, Perception: Vision. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Identify, formulate, and solve artificial intelligence problems by applying principles of engineering as well as science and mathematics; 2) Communicate effectively with a range of audiences via the lab reports and project presentations; 3) Recognize ethical and professional responsibilities in engineering situations that are directly related to artificial intelligence and related technologies while considering the impact of engineering solutions in global, economic, environmental, and societal contexts; 4) Function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives; 5) develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions for the given cases related to artificial intelligence; 6) Acquire and apply contemporary issues and methods in artificial intelligence with using appropriate learning strategies. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| 1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | ||||||
| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | ||||||
| 3) An ability to communicate effectively with a range of audiences | ||||||
| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | ||||||
| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | ||||||
| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | ||||||
| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | H | Exam,Lab,Project |
| 2) | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | N | |
| 3) | An ability to communicate effectively with a range of audiences | S | Lab,Project |
| 4) | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | H | Exam,Lab,Project |
| 5) | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | S | Project |
| 6) | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | H | Exam,Lab,Project |
| 7) | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies | H | Lab |
| Prepared by and Date | TUNA ÇAKAR , December 2018 |
| Course Coordinator | TUNA ÇAKAR |
| Semester | Fall |
| Name of Instructor |
Course Contents
| Week | Subject |
| 1) | Introduction |
| 2) | Intelligent Agents & Game Playing |
| 3) | Searching |
| 4) | Informed Search Methods |
| 5) | Constraint Satisfaction |
| 6) | Probability |
| 7) | Bayes Nets |
| 8) | Machine Learning |
| 9) | Deep Learning |
| 10) | Pattern Recognition |
| 11) | Logic and Planning |
| 12) | Planning under Uncertainty |
| 13) | Project Presentations |
| 14) | General Review |
| 15) | Final Examination/Project/Presentation Period |
| 16) | Final Examination/Project/Presentation Period |
| Required/Recommended Readings | Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell, Peter Norvig, Prentice Hall, 2010 | |||||||||||||||||||||
| Teaching Methods | Flipped classroom. Students work individually for assignments. | |||||||||||||||||||||
| Homework and Projects | Assignments and Project | |||||||||||||||||||||
| Laboratory Work | Application-based laboratory study | |||||||||||||||||||||
| Computer Use | Required | |||||||||||||||||||||
| Other Activities | None | |||||||||||||||||||||
| Assessment Methods |
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| Course Administration |
cakart@mef.edu.tr 0 212 395 37 45 Instructor’s office: 5th floor Phone number: 0 212 395 37 50 Office hours: After the lecture hours. E-mail address: cakart@mef.edu.tr Rules for attendance: YÖK Regulations. Statement on plagiarism: YÖK Regulations. http://3fcampus.mef.edu.tr/uploads/cms/webadmin.mef.edu.tr/4833_2.pdf |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 2 | 42 | |||
| Laboratory | 10 | 1 | 2 | 30 | |||
| Study Hours Out of Class | 1 | 1 | 10 | 11 | |||
| Project | 1 | 5 | 25 | 30 | |||
| Homework Assignments | 10 | 1 | 2 | 30 | |||
| Final Examination | 1 | 10 | 3 | 13 | |||
| Total Workload | 156 | ||||||
| Total Workload/25 | 6.2 | ||||||
| ECTS | 6 | ||||||