Department of Artificial Intelligence and Machine Learning
| Course Name | Introduction to Programming | ||
| Semester | Course Code | Theory Practice Hours | ECTS |
| 4 | YPZ102 | 2 / 2 | 5 |
| Course Level | Bachelor's Degree | ||
| Course Delivery Format | Face to face | ||
| Coordinator | Assoc. Prof. Dr. Hakan Yılmaz | ||
| Coordinator E-mail | hyilmaz ktun.edu.tr | ||
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| Course Objective | The objective of this course is to develop students’ algorithmic thinking and problem-solving skills, enabling them to use fundamental programming constructs through a programming language and to develop correct, understandable, structured, and testable solutions to given problems. |
| Eng Temel Meslek Dersi | Eng Uzmanlaşma Dersi |
| 80 | 20 |
| Instructional Methods |
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| Lecture, Demonstration, Question and Answer, Practicing, Problem Solving |
| Week | Course Content |
|---|---|
| 1 | Introduction to Programming and Algorithmic Thinking |
| 2 | Representation of Algorithms: Pseudocode and Flowcharts |
| 3 | Introduction to a Programming Language, Variables, and Basic Data Types |
| 4 | Operators, Expressions, and Decision Structures |
| 5 | Loops |
| 6 | Strings |
| 7 | Lists, Tuples, and Dictionaries |
| 8 | Midterm Exam |
| 9 | Introduction to Functions |
| 10 | Functions II and Built-in Functions |
| 11 | Modules and Code Organization |
| 12 | Arrays and Matrices |
| 13 | Recursive Functions |
| 14 | File Operations and Basic Exception Handling |
| 15 | Final Exam |
| No | Course Outcomes (CO) | Percent |
|---|---|---|
| 1 | Analyzes a given problem, identifies its inputs, processes, and outputs, and develops an algorithmic solution to the problem. | 25 |
| 2 | Represents algorithmic solutions using pseudocode, flowcharts, and a programming language. | 15 |
| 3 | Selects and uses appropriate variables, data types, control structures, and fundamental data structures according to problem requirements. | 25 |
| 4 | Decomposes a problem into functional subproblems and develops structured programs using functions and modular structures. | 20 |
| 5 | Tests developed programs, identifies fundamental programming errors, and applies appropriate corrections. | 15 |
| CO 1 | CO 2 | CO 3 | CO 4 | CO 5 | |
|---|---|---|---|---|---|
| PO 1 | 6 | 3 | 6 | 4 | 3 | PO 2 | 10 | 4 | 7 | 5 | 3 | PO 3 | 3 | 2 | 3 | 4 | 2 | PO 4 | 5 | 4 | 8 | 6 | 6 | PO 5 | 0 | 0 | 0 | 0 | 1 | PO 6 | 0 | 0 | 0 | 1 | 0 | PO 7 | 0 | 1 | 0 | 0 | 0 | PO 8 | 1 | 1 | 1 | 0 | 0 | PO 9 | 0 | 0 | 0 | 0 | 0 | PO 10 | 0 | 0 | 0 | 0 | 0 | PO 11 | 0 | 0 | 0 | 0 | 0 |
| ECTS Workload | Number | Duration,Min | Total Workload |
|---|---|---|---|
| Weekly Lecture Hour (Theoretical) | 2 | 45 | 90 | Weekly Practice/Lab Hour | 2 | 45 | 90 | Homework | 1 | 1500 | 1500 | Quiz | 1 | 600 | 600 | Material Design, Preparation | 0 | 0 | 0 | Field Studies | 0 | 0 | 0 | Report Preparation | 0 | 0 | 0 | Presentation | 0 | 0 | 0 | Project Making | 0 | 0 | 0 | Other Studies | 0 | 0 | 0 | Mid-Term exam and pre-study | 1 | 2000 | 2000 | Final Exam and pre-study | 1 | 2800 | 2800 |
| ECTS Workload | % of Mid-term Grading | % of Final Grading |
|---|---|---|
| Weekly Lecture Hour (Theoretical) | 0 | 0 | Weekly Practice/Lab Hour | 0 | 0 | Homework | 5 | 5 | Quiz | 5 | 5 | Material Design, Preparation | 0 | 0 | Field Studies | 0 | 0 | Report Preparation | 0 | 0 | Presentation | 0 | 0 | Project Making | 0 | 0 | Other Studies | 0 | 0 | Mid-Term exam and pre-study | 90 | 0 | Final Exam and pre-study | 0 | 90 |