ARTIFICIAL INTELLIGENCE

TKM526

Course Code: TKM526
Semester: -
Theory+Practice: 3 + 0
ECTS: 10

Course Type

Elective Course

Course Level

Master's Degree Without Thesis

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Have knowledge about basic and advanced methods in artificial intelligence and machine learning.
Will be able to model and solve practical problems using machine learning and artificial intelligence methods.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

Course Description

It is a course that aims to use machine learning and artificial intelligence applications and make inferences.

Course Contents

WEEK	TOPICS
1^st Week	Introduction to Artificial Intelligence, Basic Terms
2^nd Week	Machine Learning and Data Preprocessing
3^rd Week	Support Vector Regression and Time Series Forecasting
4^th Week	Artificial Neural Networks
5^th Week	Meta-Heuristic Optimization
6^th Week	Simulated Annealing
7^th Week	Genetic Algorithm
8^th Week	Mid-Term Exam
9^th Week	Tabu Search
10^th Week	Article Review
11^th Week	Article Review
12^th Week	Project Presentations
13^th Week	Project Presentations
14^th Week	Overview of the term

Recommended or Required Reading

T. Mitchell, "Machine Learning", McGraw-Hill, 1997.
C. M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2007.
N. Gürsakal, "Makine Öğrenmesi", Dora Yayın, 2018.
V. Nabiev, "Yapay Zeka", Seçkin Yayınevi, 2021.
G. Bektaş vd., "Yapay Zeka Optimizasyon Algoritmaları ve Mühendislik Uygulamaları", Seçkin Yayınevi 2021.
M. Kubat, "Introduction to Machine Learning", Springer, 2017.

Planned Learning Activities and Teaching Methods

Lecture
Practice

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	30
Project	1	30
Total(%)		60
Contribution of In-term Studies to Overall Grade(%)		60
Contribution of Final Examination to Overall Grade(%)		40
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	2	2
Preparation for Quiz
Individual or group work	14	8	112
Preparation for Final exam	1	50	50
Course hours	14	3	42
Preparation for Midterm exam	1	50	50
Laboratory (including preparation)
Final exam	1	2	2
Homework
Project	1	30	30
Total Work Load			288
Total Work Load / 30 (h)			9.6
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2
K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
K12
K13