Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
2	BLP114	Artificial İntelligence	3+0+0	3	6

Course Details

Language of Instruction	Turkish
Level of Course Unit	Associate Degree
Department / Program	Computer Programming
Mode of Delivery	Face to Face
Type of Course Unit	Compulsory
Objectives of the Course	To provide a solid foundation in artificial intelligence and machine learning. The course will introduce the basic features of the Python programming language and show how it can be used for artificial intelligence applications. Students will learn basic machine learning algorithms and understand how these algorithms can be applied to various problems. At the end of the course, students will be able to develop artificial intelligence and machine learning projects.
Course Content	Introduction to Python programming language, basic functions, operators, conditional statements, loops, debugging and ready-made modules. Introduction to artificial intelligence, basic concepts, important libraries (numpy, pandas, matplotlib), regression, classification and clustering concepts and the most basic machine learning algorithms related to these concepts.
Course Methods and Techniques	Theoretical lectures, practical applications and laboratory studies.
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Instructor Zeki ÇIPLAK https://abis.gedik.edu.tr/zeki-ciplak zeki.ciplak@gedik.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	"Introduction to Machine Learning with Python" by Andreas C. Müller and Sarah Guido "Data Science from Scratch: First Principles with Python" by Joel Grus
	PDF notes shared at the end of the lecture, question-answer practice.

	Bir grup projesi
	Vize ve Final Sınavları

Course Category

Mathematics and Basic Sciences	%10
Engineering	%5
Engineering Design	%10
Social Sciences	%0
Education	%0
Science	%0
Health	%0
Field	%75

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Course Duration	14	3	42
Hours for off-the-c.r.stud	14	5	70
Mid-terms	1	11	11
Final examination	1	30	30
Total Work Load			Number of ECTS Credits 6 153

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Learns basic Python programming language skills.
2	Gain an introductory perspective on Artificial Intelligence problems.
3	Gain the ability to develop solutions to problems.
4	Using basic machine learning algorithms.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Giving information about the general functioning of the course and the curriculum and introductions.
2	Introduction to Python programming language, installations and basic functions.
3	Basic data types, operators, conditional statements and exercises in Python.
4	Exercises about List, Tuple, Set and Dict data types and their usage in Python.
5	Type conversions, loops and functions in Python.
6	Using embedded functions and ready-made modules in Python.
7	General Recap.
8	Midterm exam.
9	Exercises on debugging in Python, file operations and JSON file format.
10	Introduction to artificial intelligence, basic concepts and use of numpy, pandas and matplotlib libraries.
11	Use of Numpy, Pandas and Matplotlib libraries in data organization.
12	Simple data preprocessing, regression and model evaluation.
13	Classification and model evaluation. Model building and evaluation exercises.
14	Clustering and the working principle of the K-Means algorithm.
15	General Recap.

Contribution of Learning Outcomes to Programme Outcomes

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https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=113552&curProgID=44&lang=en