COURSE UNIT TITLE

: SUPERVISED STATISTICAL LEARNING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5007 SUPERVISED STATISTICAL LEARNING ELECTIVE 3 0 0 8

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 statsitical leraning (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, splines, general additive models, 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

1   Fit a regression model for binary and continuous response and make predictions
2   Classify data by using distance based methods.
3   Construct piecewise models
4   Build a model for non-linear functions of input variables
5   Analyzing data using tree based methods
6   Perform diagnostics for a model
7   Evaluate performance of a model
8   Use R to build, visualize and test performance of a model

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction, Simple Linear Regression
2 Multiple Linear Regression
3 Model selection
4 Diagnostics (Outliers, Influential Observations, multicollinearity, heteroscedasticity, normality)
5 Logistic Regression-Estimation, Diagnostics
6 Logistic regression-Model selection, prediction( ROC curve)
7 Assignment Presentations
8 Midterm
9 Linear Discriminant Analysis
10 Quadratic Discriminant Analysis
11 Regression Trees
12 Classification Trees
13 Random Forests
14 Assignment Presentations

Recomended or Required Reading

Textbook(s):
Balaban, M.E., Kartal, E. (2015). Veri Madenciliği ve Makine Öğrenmesi. Çağlayan Kitabevi.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer, New York.

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

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 MTE MIDTERM EXAM
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE ASG * 0.30 + MTE * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.30 + MTE * 0.30 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Assoc. Prof. Dr. Neslihan DEMIREL
Dokuz Eylul University, Faculty of Sciences, Department of Statistics,
Tinaztepe Campus, 35390, Buca-Izmir
Room number: B-005
e-mail: neslihan.ortabas@deu.edu.tr
Phone: +90.232.3018573

Office Hours

Send an e-mail for a meeting request.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 20 20
Preparation for final exam 1 40 40
Preparing assignments 2 20 40
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 190

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.1545555
LO.2545555
LO.3545555
LO.4545555
LO.5545555
LO.6545555
LO.7545555
LO.85555555