Information Package / Course Catalogue
| Psychology | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| 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 | |||||
| Expected Prior Knowledge | Basic mathematics knowledge | |||||
| Co-requisites | None | |||||
| 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 Description in Turkish | Bu derste; yapay zekanın temel kavramları şu konu başlıklar altında kapsamlı bir şekilde incelenmektedir: Akıllı etmenler, arama yöntemleri, A* arama ve sezgisel arama yötemleri, kısıt altında arama yöntemleri, oyun ağaçları, bilgi gösterimi, öğrenme, güdümlü öğrenme, karar ağaçları, evrimsel yöntemler, Yapay Sinir Ağları (YSA) , Perseptronlar ve Derin Öğrenme, Algılama:Yapay Görü. |
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) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||||
| 6) Internalization and dissemination of professional ethical standards. | ||||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
| Prepared by and Date | TUNA ÇAKAR , December 2018 |
| Course Coordinator | TUBA AYHAN |
| Semester | Fall |
| Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Course Contents
| Week | Subject |
| 1) | |
| 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 Period |
| 16) | Final Examination 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 & 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 Office hours: After the lecture hours. Rules for attendance: No attendance required. Statement on plagiarism: YÖK Regulations |
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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 | ||||||