Akademik Dersler - Tanıtım


Code - Course Title	İST438 - Machine Learning Methods and Applications
Course Type	Area Elective Courses
Language of Instruction	İngilizce
Laboratory + Practice	3+0
ECTS	4.5
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ MUSTAFA ÇAVUŞ
Mode of Delivery	This course will only face-to-face training.
Prerequisites	There is no prerequisite or co-requisite for this course.
Courses Recomended	There are no suggested lessons related with this course.
Recommended Reading List	Hastie, T., Tibshirani, R., and Friedman, J. (2009) The Elements of Statistical Learning, Springer-Verlag New York.
Assessment methods and criteria	2 Midterm Exam and 1 Final Exam
Work Placement	This is not appropriate for the course.
Sustainability Development Goals

Weeks	Topics
Week - 1	Introduction to Machine Learning
Week - 2	Linear Regression Model
Week - 3	Variable Selection and Regularization
Week - 4	Cross-Validation and Model Evaluation
Week - 5	Supervised Learning Methods
Week - 6	Logistic and Ordered Regression Models
Week - 7	k-nearest Neighbors Algorithm
Week - 8	Decision Trees
Week - 9	Ensemble Learning Methods
Week - 10	Random Forests
Week - 11	Unsupervised Learning Methods
Week - 12	k-means Algorithm
Week - 13	Principal Component Analysis
Week - 14	Project Presentations

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
1.Midterm Exam	1	20
Homework	1	30
Final Exam	1	50
Toplam (%)		100