| ARTIFICIAL INTELLIGENCE ENGINEERING | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| AIN1001 | Computer Programming I (Python) | Fall | 2 | 2 | 3 | 5 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Must Course |
| Course Level: | Bachelor |
| Mode of Delivery: | Hybrid |
| Course Coordinator : | Assist. Prof. BİNNUR KURT |
| Course Objectives: | This course introduces the Python programming language with an object-oriented approach. Students will be able to acquire basic programming skills using Python language and develop applications to solve engineering problems in an integrated development environment (IDE). |
|
The students who have succeeded in this course; Within completing this course, the student will be able to: 1. Define the basic concepts in Python programming language. 2. Develop, test, and run programs in order to solve a specific software problem. 3. Define the basic data types. 4. Define arithmetic, logical expressions, type conversations, assignment expressions, selection, and iteration controls. 5. Demonstrate the ability to create and use functions. 6. Demonstrate the ability to use logical control expressions. 7. Define the datasets. 8. Demonstrate the ability to use libraries prepared for Python when developing applications. |
| This course provides an introduction to fundamental programming concepts using Python. Students will learn problem-solving techniques, algorithm development, and the basics of writing efficient code. Key topics include data types, decision structures, functions, loops, strings, arrays, and file handling. The course also covers exception handling and introduces students to best practices in coding. Through hands-on exercises and projects, students will develop the ability to write, debug, and optimize Python programs. The course will be conducted through lectures and class discussions, hands-on laboratory sessions, and individual/group project work. Course Content: - Introduction to Computers and Programming - Algorithms, Flowcharts, Pseudocodes - Data Types and Expressions - Decision Structures - Functions - Strings - Loops - Functions - Arrays & Lists - Files - Exception Handling |
| Week | Subject | Related Preparation | |
| 1) | Orientation | -Course Schedule -Expectations | |
| 2) | Introduction to Computers and Programming - Algorithms, Flowcharts, Pseudocodes | -Set up your environment -Read Chapters 1 & 2 | |
| 3) | Data Types and Expressions | Chapter 1 & 2 | |
| 4) | Decision Structures | Chapter 3 | |
| 5) | Loops | Chapter 4 | |
| 6) | Functions | Chapter 5 | |
| 7) | Functions | Chapter 5 | |
| 8) | MIDTERM & Lab Exam #1 | All course materials | |
| 9) | Arrays & Lists | Chapter 7 | |
| 10) | Strings | Chapter 8 | |
| 11) | Files | Chapter 6 | |
| 12) | Exception Handling - Lab Exam #2 | Chapter 6 | |
| 13) | Case Study I – Shopping Cart | - | |
| 14) | Case Study II – Gas Prices | ||
| Course Notes: | Recommended Textbook: Tony Gaddis, "Starting out With Python", 4th edition, Pearson |
| References: | Recommended Textbook: Tony Gaddis, "Starting out With Python", 4th edition, Pearson |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | 2 | % 30 |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | % 0 | |
| Seminar | % 0 | |
| Midterms | 1 | % 30 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 2 | 28 |
| Laboratory | 14 | 2 | 28 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 0 | 0 | 0 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 2 | 6 | 12 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 1 | 20 | 20 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 1 | 28 | 28 |
| Total Workload | 116 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Build up a body of knowledge in mathematics, science and Artificial Intelligence Engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems. | 5 |
| 2) | Design complex Artificial Intelligence systems, platforms, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. | |
| 3) | Identify, formulate, and solve complex Artificial Intelligence Engineering problems; select and apply proper modeling and analysis methods for this purpose. | |
| 4) | Devise, select, and use modern techniques and tools needed for solving complex problems in Artificial Intelligence Engineering practice; employ information technologies effectively. | 5 |
| 5) | Design and conduct numerical or physical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Artificial Intelligence Engineering. | |
| 6) | Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. Write and understand reports, prepare design and production reports, deliver effective presentations, give and receive clear and understandable instructions. | |
| 7) | Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself. | |
| 8) | Develop an awareness of professional and ethical responsibility, and behave accordingly. Be informed about the standards used in Artificial Intelligence Engineering applications. | |
| 9) | Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development. | |
| 10) | Acquire knowledge about the effects of practices of Artificial Intelligence Engineering on health, environment, security in universal and social scope, and the contemporary problems of Artificial Intelligence Engineering; is aware of the legal consequences of Mechatronics engineering solutions. | |
| 11) | Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Artificial Intelligence-related problems. |