Electrical and Electronics Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Electrical and Electronics Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Electrical and Electronics Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05150505 | Introduction to Artificial ıntelligence | 3 | Autumn | 5 | 3+0+0 | 3 | 3 |
| Course Type | Compulsory |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | 1. Theoretical Explanation: The topics are explained theoretically within the scope of the course. Students listen to topic explanations in order to understand the basic concepts of programming and the logic of algorithms. 2. Applied Studies: Students carry out studies with various examples under the mentorship of the course instructor in order to apply the topics explained theoretically. Gains are tried to be achieved. 3. Step-by-Step Solution: The encountered problems are solved step by step and how each step works is explained. With this method, students are provided with a better command of the topics. 4. Real Life Examples: Real life examples and problem scenarios are presented for a better understanding of the topics. In this way, students see how to use what they have learned in practice. 5. Laboratory Sheets and Quizzes: Students' progress is evaluated with weekly laboratory handouts and pre-exam quizzes, and whether the topics are understood is monitored. |
| Mode of Delivery | Face to Face |
| Prerequisites | There are no prerequisites for the course. All students are provided with education starting from the basic level. |
| Coordinator | - |
| Instructor(s) | Lect. Uğur POLAT |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Uğur POLAT | -A122 TSMYO T213 | [email protected] | 7860 | Monday 15:00-16:00 |
Course Content
Artificial Intelligence Basic Concepts
Agents
Problem Solving and Search
Uninformed Search Methods
Informed Search Methods
Introduction to Machine Learning
Classification Algorithms
Clustering and Dimensionality Reduction
Introduction to Deep Learning
Convolutional Neural Networks (CNN)
Natural Language Processing (NLP)
Reinforcement Learning
Debate and Evaluation
Agents
Problem Solving and Search
Uninformed Search Methods
Informed Search Methods
Introduction to Machine Learning
Classification Algorithms
Clustering and Dimensionality Reduction
Introduction to Deep Learning
Convolutional Neural Networks (CNN)
Natural Language Processing (NLP)
Reinforcement Learning
Debate and Evaluation
Objectives of the Course
The aim of this course is to provide students with the basic concepts of artificial intelligence (AI), to understand how AI works, its basic algorithms and application areas. Students will combine theoretical knowledge with practical applications by introducing sub-disciplines such as the history of AI, machine learning, deep learning, natural language processing and computer vision.
At the end of the course, students will be able to recognize the basic algorithms of AI, develop algorithms working with different types of data, discuss ethical and social impacts, and reach a level where they can produce solutions to real-world problems. The course provides a basic framework for students who want to understand the scientific and technical aspects of AI, while also preparing them for more advanced studies in the field of AI in the future.
At the end of the course, students will be able to recognize the basic algorithms of AI, develop algorithms working with different types of data, discuss ethical and social impacts, and reach a level where they can produce solutions to real-world problems. The course provides a basic framework for students who want to understand the scientific and technical aspects of AI, while also preparing them for more advanced studies in the field of AI in the future.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | X |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | Solid knowledge base in mathematics, natural sciences, and engineering-related subjects, along with the ability to solve complex engineering problems using this knowledge. | 5 |
| P2 | Ability to identify, describe, mathematically express, and solve challenging engineering problems; the capability to select and utilize appropriate analysis and modeling techniques for this purpose. | 5 |
| P5 | Ability to plan experiments, conduct them, collect data, analyze and interpret results regarding complex engineering problems or discipline-specific research topics. | 4 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Have the skills to develop approximate solution methods to engineering problems. | P.1.2 | 1,2,5 |
| O2 | Performs computer-aided analysis and calculations. | P.4.4 | |
| O3 | Acquires the ability to work in a team. | P.6.2 | 2,5 |
| O4 | Works with different disciplines on an engineering problem and prepares a report for the solution of the problem. | P.6.3 | 2,5 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Artificial Intelligence Basic Concepts |
| 2 | Agents |
| 3 | Problem Solving and Searching |
| 4 | Uninformed Search Methods |
| 5 | Informed Search Methods |
| 6 | Introduction to Machine Learning |
| 7 | Pre-Exam Quiz and General Review |
| 8 | Mid-term Exam |
| 9 | Classification Algorithms |
| 10 | Clustering and Dimensionality Reduction |
| 11 | Introduction to Deep Learning |
| 12 | Convolutional Neural Networks (CNN) |
| 13 | Natural Language Processing (NLP) |
| 14 | Reinforcement Learning |
| 15 | Pre-Exam Quiz and Debate |
| 16 | Final Exam |
Textbook or Material
| Resources | Stuart Russell, Peter Norvig,"Artificial Intelligence-A Modern Approach", 4th Edition, Pearson |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | - | - |
| Presentation | 1 | 20 (%) |
| Projects | - | - |
| Quiz | 2 | 10 (%) |
| Listening | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 40 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 16 | 3 | 48 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 1 | 14 |
| Midterms | 1 | 7 | 7 |
| Quiz | 2 | 2 | 4 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 1 | 10 | 10 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 16 | 16 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 99 | ||
| Total Work Load / 30 | 3,30 | ||
| Course ECTS Credits: | 3 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P4 | P6 |
|---|---|---|---|---|
| O1 | Have the skills to develop approximate solution methods to engineering problems. | 5 | 5 | 4 |
| O2 | Performs computer-aided analysis and calculations. | 5 | 5 | 4 |
| O3 | Acquires the ability to work in a team. | 5 | 5 | 4 |
| O4 | Works with different disciplines on an engineering problem and prepares a report for the solution of the problem. | 5 | 5 | 4 |