ECTS - Artificial Intelligence
Artificial Intelligence (MECE441) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Artificial Intelligence | MECE441 | Area Elective | 3 | 0 | 0 | 3 | 6 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Question and Answer, Problem Solving, Project Design/Management. |
| Course Lecturer(s) |
|
| Course Objectives | The primary objective of this course is to provide an introduction to the basic principles, techniques, and applications of Artificial Intelligence. Throughout this course, besides the techniques to develop intelligence, the difficulties encountered in design of intelligent mechatronic products and proposed solution strategies are also studied. |
| Course Learning Outcomes |
The students who succeeded in this course;
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| Course Content | Introduction to artificial intelligence, state-space search; uninformed (Blind) search techniques, informed (heuristic) search techniques, logical reasoning: propositional logic, predicate calculus, probabilistic reasoning, Bayes rule, reasoning under uncertainty, knowledge-based systems: rule-based expert systems, introduction to machine learning, |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to artificial intelligence | N/A |
| 2 | State Space Search; Uninformed (Blind) Search Techniques | N/A |
| 3 | State Space Search; Informed (Heuristic) Search Techniques | N/A |
| 4 | Logical Reasoning: Propositional Logic, Predicate Calculus | N/A |
| 5 | Probabilistic reasoning, Bayes Rule | N/A |
| 6 | Reasoning under uncertainty | N/A |
| 7 | Knowledge-Based Systems: Rule-based Expert Systems | N/A |
| 8 | Introduction to Machine Learning | N/A |
| 9 | Belief networks | N/A |
| 10 | Supervised learning methods | N/A |
| 11 | Semantic Nets, Reinforcement learning | N/A |
| 12 | Genetic Algorithms | N/A |
| 13 | Genetic Algorithms (continued) | N/A |
| 14 | Case Studies | N/A |
| 15 | Case Studies | N/A |
| 16 | Final Examination | N/A |
Sources
| Course Book | 1. Russell, S. and Norvig, P., Artificial Intelligence: A Modern Approach, Pearson Education, 2010. |
|---|
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 3 | 15 |
| Presentation | - | - |
| Project | 1 | 30 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 25 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 6 | 100 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | An ability to apply knowledge in mathematics and basic sciences and computational skills to solve manufacturing engineering problems | |||||
| 2 | An ability to define and analyze issues related with manufacturing technologies | |||||
| 3 | An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment | |||||
| 4 | An ability to design a comprehensive manufacturing system based on creative utilization of fundamental engineering principles while fulfilling sustainability in environment and manufacturability and economic constraints | |||||
| 5 | An ability to chose and use modern technologies and engineering tools for manufacturing engineering applications | |||||
| 6 | An ability to utilize information technologies efficiently to acquire datum and analyze critically, articulate the outcome and make decision accordingly | |||||
| 7 | An ability to attain self-confidence and necessary organizational work skills to participate in multi-diciplinary and interdiciplinary teams as well as act individually | |||||
| 8 | An ability to attain efficient communication skills in Turkish and English both verbally and orally | |||||
| 9 | An ability to reach knowledge and to attain life-long learning and self-improvement skills, to follow recent advances in science and technology | |||||
| 10 | An awareness and responsibility about professional, legal, ethical and social issues in manufacturing engineering | |||||
| 11 | An awareness about solution focused project and risk management, enterpreneurship, innovative and sustainable development | |||||
| 12 | An understanding on the effects of engineering applications on health, social and legal aspects at universal and local level during decision making process | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 14 | 3 | 42 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 2 | 28 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 34 | 34 |
| Report | |||
| Homework Assignments | 3 | 2 | 6 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 5 | 5 |
| Prepration of Final Exams/Final Jury | 1 | 5 | 5 |
| Total Workload | 120 | ||