TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree Without Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Professor Mehmet Güray Ünsal
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Have knowledge about basic and advanced methods in artificial intelligence and machine learning. 2) Will be able to model and solve practical problems using machine learning and artificial intelligence methods.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
COURSE DEFINITION	It is a course that aims to use machine learning and artificial intelligence applications and make inferences.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to Artificial Intelligence, Basic Terms 2nd Week Machine Learning and Data Preprocessing 3rd Week Support Vector Regression and Time Series Forecasting 4th Week Artificial Neural Networks 5th Week Meta-Heuristic Optimization 6th Week Simulated Annealing 7th Week Genetic Algorithm 8th Week Mid-Term Exam 9th Week Tabu Search 10th Week Article Review 11th Week Article Review 12th Week Project Presentations 13th Week Project Presentations 14th Week Overview of the term
RECOMENDED 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 WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 3 42 Preparation for Final exam 1 25 25 Course hours 14 3 42 Preparation for Midterm exam 1 25 25 Laboratory (including preparation) Final exam 1 2 2 Homework Project 1 12 12 Total Workload 150 Total Workload / 30 5 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No