TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
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 methods in machine learning. 2) Will be able to model and solve practical problems using machine learning 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 basic machine learning applications and make inferences.
COURSE CONTENTS	WEEK TOPICS 1st Week Basic concepts, Introduction to Machine Learning 2nd Week Types of Machine Learning and Performance Evaluation 3rd Week Data Preprocessing 4th Week Supervised Learning 1 (Multiple Regression, Ridge Regression ve LASSO Based Machine Learning) 5th Week Supervised Learning 2 (k-Nearest Neighborhood) 6th Week Supervised Learning 3 (Support Vector Machines) 7th Week Supervised Learning 4 (Naive Bayesian Classification) 8th Week Mid-Term Exam 9th Week Supervised Learning 5 (Binary Lojistic Regression) 10th Week Unsupervised Learning 1 (Principle Component Analysis) 11th Week Unsupervised Learning 2 (K-Means) 12th Week Ensemble Learning (Random Forest) 13th Week Selection of the best model (-K-Folds Cross Validation) and Parameter Tuning 14th Week AdaBoost (Adaptive Boosting), Gradient Boosting and XGBoost (Extreme Gradient Boosting) and Project Presentations
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. ME. Balaban, E. Kartal, Veri Madenciliği ve Makine Öğrenmesi, Çağlayan Yayıncılık, 2018. 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 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No