COURSE UNIT TITLE

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR NESLIHAN DEMIREL

Offered to

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

Supervised statistical learning (SSL) involves building a statistical model for predicting, or estimating, an output based on one or more inputs. SSL tools can be categorised as regression and classification. This course will include most popular regression and classification methods such as linear regression model, logistic regression model, linear and quadratic discrimanant analysis, regression and classification trees and random forest. Students will also have a good sense for how to evaluate performance of those methods. They will be using R for analyzing the data with tools of SSL.

Learning Outcomes of the Course Unit

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Recomended or Required Reading

Textbook(s):
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R. Second Ed., Springer, New York.
Balaban, M.E., Kartal, E. (2015). Veri Madenciliği ve Makine Öğrenmesi. Çağlayan Kitabevi.

Supplementary Book(s):
Albalate, A. & Minker, W. (2011). Semi-Supervised and Unsupervised Machine Learning. John Wiley & Sons, Inc., London.
Alpar R. (2017). Uygulamalı Çok değişkenli Istatistiksel Yöntemler. Detay Yayıncılık.
Apaydın, E. (2011). Yapay Öğrenme. Boğaziçi Üniversitesi Yayınevi.
Friedman J., Hastie, T., Tibshirani, R. (2013). The Elements of Statistical Learning Data Mining, Inference and Prediction Preface to the Second Edition

Planned Learning Activities and Teaching Methods

Lecture, class discussion, homeworks.

Assessment Methods

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homeworks/reports/presentations/exams.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the graduate policy at http://www.fbe.deu.edu.tr/

Contact Details for the Lecturer(s)

Prof. Dr. Neslihan DEMIREL
Dokuz Eylul University, Faculty of Sciences, Department of Statistics,
Tinaztepe Campus, 35390, Buca-Izmir
Room number: B-231
e-mail: neslihan.ortabas@deu.edu.tr
Phone: +90.232.3018560

Office Hours

Send an e-mail for a meeting request.

Work Placement(s)

None

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes