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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS VYA 5026 STATISTICAL LEARNING COMPULSORY 3 0 0 6

Offered By

DATA MANAGEMENT AND ANALYSIS

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR MURAT TANIK

Offered to

DATA MANAGEMENT AND ANALYSIS

Course Objective

The aim of the course is to give students the most common statistical learning techniques based on practice.

Learning Outcomes of the Course Unit

1   To be able to apply statistical learning techniques in R programs. 2   To be able to make statistical predictions on large-scale problems. 3   To be able to choose appropriate estimation methods depending on the structure of the relationships between the variables. 4   To be able to establish linear and nonlinear (non-parametric) models and to make the necessary analysis. 5   To be able to use up-to-date computing facilities for analysis.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to Statistical Learning 2 Assessing Model Accuracy 3 Linear Regression 4 Multiple Linear Regression 5 Classification: Logistic Regression and Discriminant Analysis 6 R Applications for Regression and Classification Problems 7 Resampling Methods: Cross-Validation and Bootstrap 8 Linear Model Selection 9 Ridge Regression and Lasso 10 Dimension Reduction Methods 11 Nonparametric Regression 12 Generalized Additive Models 13 Case Studies 14 Case Studies

Recomended or Required Reading

To be announced.

Planned Learning Activities and Teaching Methods

Assessment Methods

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

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 10 3 30 Preparation for midterm exam 1 20 20 Preparation for final exam 1 20 20 Preparing assignments 5 5 25 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 143

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 LO.1 1 LO.2 1 LO.3 1 1 LO.4 1 LO.5