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 | Applies knowledge in mathematics, science, and computing to solve engineering problems related to manufacturing technologies. | |||||
| 2 | Analyzes and identifies problems specific to manufacturing technologies. | |||||
| 3 | Develops an approach to solve encountered engineering problems, and designs and conducts models and experiments. | |||||
| 4 | Designs a comprehensive manufacturing system (including method, product, or device development) based on the creative application of fundamental engineering principles, within constraints of economic viability, environmental sustainability, and manufacturability. | |||||
| 5 | Selects and uses modern techniques and engineering tools for manufacturing engineering applications. | |||||
| 6 | Effectively uses information technologies to collect and analyze data, think critically, interpret, and make sound decisions. | |||||
| 7 | Works effectively as a member of multidisciplinary and intra-disciplinary teams or individually; demonstrates the confidence and necessary organizational skills. | |||||
| 8 | Communicates effectively in both spoken and written Turkish and English. | |||||
| 9 | Engages in lifelong learning, accesses information, keeps up with the latest developments in science and technology, and continuously renews oneself. | |||||
| 10 | Demonstrates awareness and a sense of responsibility regarding professional, legal, ethical, and social issues in the field of Manufacturing Engineering. | |||||
| 11 | Effectively utilizes resources (personnel, equipment, and costs) to enhance national competitiveness and improve manufacturing industry productivity; conducts solution-oriented project and risk management; and demonstrates awareness of entrepreneurship, innovation, and sustainable development. | |||||
| 12 | Considers the health, environmental, social, and legal consequences of engineering practices at both global and local scales when making decisions. | |||||
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 | ||