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 |
|---|---|---|---|---|---|---|---|
| 15140004 | Introduction to Artificial ıntelligence | 2025 | Spring | 4 | 3+0+0 | 3 | 4 |
| 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 | 758 1. Theoretical Explanation: Topics are explained theoretically within the scope of the course. Students listen to lectures to understand the basic concepts of programming and the logic of algorithms. 2. Practical Exercises: Students work with various examples under the mentorship of the course instructor to apply the topics explained theoretically. The aim is to achieve learning outcomes. 3. Step-by-Step Solution: Problems are solved step-by-step, and how each step works is explained. This method helps students gain a better understanding of the topics. 4. Real-Life Examples: Real-life examples and problem scenarios are presented to help students better understand the topics. This allows students to see how to apply what they have learned in practice. |
| Mode of Delivery | Face to Face |
| Prerequisites | There are no prerequisites for the course. All students receive instruction starting from the basic level. |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Atakan DAŞDEMİR |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Atakan DAŞDEMİR | A-127 | [email protected] | 7479 | 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 objective of the course is to comprehensively teach students the fundamental concepts of artificial intelligence, problem-solving strategies, and modern learning algorithms. By grasping intelligent agent architectures and search methods (informed/uninformed), students will develop their algorithmic thinking frameworks. Furthermore, starting from the fundamentals of machine learning and deep learning, they will gain a theoretical foundation in contemporary technologies such as CNN, NLP, and reinforcement learning, thereby acquiring the ability to produce innovative solutions for complex data-driven problems and systems requiring autonomous decision orchestration.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| 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 | 1,6 |
| O2 | Understanding the principles of Artificial Neural Networks and learning their differences from traditional programs. | P.1.83 | 1,6 |
| O3 | Understanding and learning the basic structure of Artificial Neural Networks and various network structures. | P.2.71 | 1,6 |
| O4 | Learning simple Artificial Neural Networks training methods | P.3.18 | 1,6 |
| O5 | Performs computer-aided analysis and calculations. | P.4.4 | 1 |
| O6 | Learning how to prepare and run an Artificial Neural Networks project | P.4.32 | 1,6 |
| ** 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 | - | - |
| Projects | 1 | 60 (%) |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | - | - |
| 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 | 20 | 20 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 1 | 17 | 17 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 0 | 0 | 0 |
| 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 | P2 | P3 | P4 |
|---|---|---|---|---|---|
| O1 | Have the skills to develop approximate solution methods to engineering problems. | 5 | - | - | - |
| O2 | Understanding the principles of Artificial Neural Networks and learning their differences from traditional programs. | 5 | - | - | - |
| O3 | Understanding and learning the basic structure of Artificial Neural Networks and various network structures. | - | 5 | - | - |
| O4 | Learning simple Artificial Neural Networks training methods | - | - | 5 | - |
| O5 | Performs computer-aided analysis and calculations. | - | - | - | 5 |
| O6 | Learning how to prepare and run an Artificial Neural Networks project | - | - | - | 5 |