MACHINE LEARNING

TKM439

Course Code: TKM439
Semester: -
Theory+Practice: 3 + 0
ECTS: 5

Course Type

Elective Course

Course Level

Bachelor's Degree

Year of Study

Semester

ECTS

Name of Lecturer(s)

Professor Mehmet Güray Ünsal

Learning Outcomes

At the end of this course, the students;

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

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

Course Description

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

Course Contents

WEEK	TOPICS
1^st Week	Basic concepts, Introduction to Machine Learning
2^nd Week	Types of Machine Learning and Performance Evaluation
3^rd Week	Data Preprocessing
4^th Week	Supervised Learning 1 (Multiple Regression, Ridge Regression ve LASSO Based Machine Learning)
5^th Week	Supervised Learning 2 (k-Nearest Neighborhood)
6^th Week	Supervised Learning 3 (Support Vector Machines)
7^th Week	Supervised Learning 4 (Naive Bayesian Classification)
8^th Week	Mid-Term Exam
9^th Week	Supervised Learning 5 (Binary Lojistic Regression)
10^th Week	Unsupervised Learning 1 (Principle Component Analysis)
11^th Week	Unsupervised Learning 2 (K-Means)
12^th Week	Ensemble Learning (Random Forest)
13^th Week	Selection of the best model (-K-Folds Cross Validation) and Parameter Tuning
14^th Week	AdaBoost (Adaptive Boosting), Gradient Boosting and XGBoost (Extreme Gradient Boosting) and Project Presentations

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.
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 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	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 Work Load			150
Total Work Load / 30 (h)			5
ECTS Credit of the Course			5

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2
K1	X	X
K2	X
K3		X
K4
K5	X	X
K6
K7
K8		X
K9
K10
K11
K12
K13
K14	X	X
K15		X