| 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 |
| Lecture: 2 |
Recitation: none |
Lab: 2 |
Other: none |
|
| 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 Competences
Upon 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.
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| 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 |
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| 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 |
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| 3) An ability to communicate effectively with a range of audiences |
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| 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 |
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| 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 |
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| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
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| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
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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 |
Asst. Prof. Dr. TUNA ÇAKAR |
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 Uncertanity |
| 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 |
| Assessment Tools |
Count |
Weight |
| Application |
10 |
% 30 |
| Quiz(zes) |
2 |
% 10 |
| Homework Assignments |
1 |
% 10 |
| Project |
1 |
% 20 |
| Final Examination |
1 |
% 30 |
| TOTAL |
% 100 |
|
| 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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Information Package / Course Catalogue