MATH 624 | Course Introduction and Application Information - Applied Mathematics and Statistics – With Thesis

Course Name

Introduction to Statistical Machine Learning

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MATH 624	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Third Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Dr.Öğretim Üyesi NECLA KOÇHAN
Course Lecturer(s)	Dr.Öğretim Üyesi NECLA KOÇHAN
Assistant(s)	Arş.Gör. Merve KAHRAMAN ARİMAN

Course Objectives	The objective of this course is to teach students the concepts of machine learning and machine learning algorithms, as well as to provide experience by applying these algorithms to real datasets.
Learning Outcomes	The students who succeeded in this course; describe the fundamental principles of machine learning. compare different machine learning methods. explain which parameters to use when selecting and applying a machine learning method. perform parameter optimization when applying methods. apply machine learning methods on a given dataset using R programming and/or Python apply survival analysis to real datasets.
Course Description	In this course, machine learning concepts, supervised learning, classification methods, regression methods, unsupervised learning, clustering methods, and survival analysis topics will be examined.
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Introduction to Statistical Learning	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 1-2
2	Introduction to Statistical Learning	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 1-2
3	Simple Linear Regression	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 3
4	Multiple Linear Regression	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 3
5	Classification	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 4
6	Classification	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 4
7	Cross validation and bootstrapping	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 6
8	Midterm
9	Regularization	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 6
10	Tree based methods	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 8
11	Support Vector Machines	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 9
12	Survival Analysis	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 11
13	Unsupervised Learning	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 12
14	Unsupervised Learning	“An Introduction to Statistical Learning: With Applications in R” by G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd Edition, Springer. Ch. 12
15	Semester Review
16	Final Exam

Course Notes/Textbooks

G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning: With Applications in R, 2nd Edition, ISBN- 13: 978-1259717604, Springer

Suggested Readings/Materials

T.M. Mitchell. Machine Learning, McGraw Hill, 1997, ISBN-13: 978-0070428072. C.M. Bishop. Pattern Recognition and Machine Learning, Springer, 2007, ISBN-13: 978-0387310732. E. Alpaydın. Introduction to Machine Learning, 1st Edition, The MIT Press, 2004, ISBN-13: 978-0262012119

T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning, 2nd Edition, Springer, 2009, ISBN-13: 978-0387848570.

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury	1	10
Project	1	20
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	3	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	16	3	48
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	18	18
Project	1	44	44
Seminar / Workshop			0
Oral Exam			0
Midterms	1	22	22
Final Exam	1	45	45
		Total	225

#

Program Competencies/Outcomes

* Contribution Level

1

2

3

4

5

1

To be able to demonstrate independent and critical thinking in Applied Mathematics and Statistics.

-

X

-

2

To be able to define problems in Applied Mathematics/Statistics and verify whether they are mathematically/statistically consistent.

-

3

To be able to analyse and solve real life problems using applied methods and interdisciplinary approach of Mathematics/Statistics.

-

X

-

4

To be able to independently conduct, conclude, and report on specialized research in Applied Mathematics and Statistics.

-

X

-

5

To be able to efficiently use national and international resources, for staying updated in the field, communicating with colleagues, and following the related literature.

-

6

To be able to develop proficiency in using computer software widely utilized in the fields of Applied Mathematics and Statistics.

-

X

-

7

To be able to evaluate solution processes efficiently using mathematical reasoning and modeling in order to contribute to the solutions of social and scientific problems.

-

8

To be able to synthesize theoretical frameworks with practical applications through mathematical and statistical methods.

-

9

To be able to develop strategies, policies and plans for problems and research areas in Applied Mathematics/Statistics in order to interpret the results and translate them into practice.

-

10

To be able to translate key topics, events, and phenomena in Applied Mathematics and Statistics into the context of other scientific disciplines.

-

X

-

11

To be able to engage in lifelong learning by continuously updating and improving knowledge and skills in Applied Mathematics and Statistics.

-

MATH 624 | Course Introduction and Application Information

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

EVALUATION SYSTEM

ECTS / WORKLOAD TABLE

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

IZMIR UNIVERSITY OF ECONOMICS GÜZELBAHÇE CAMPUS

GLOBAL CAREER

CONTRIBUTION TO SCIENCE

VALUING PEOPLE

BENEFIT TO SOCIETY