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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS VYA 5022 SUPERVISED MACHINE 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 SERKAN ARAS

Offered to

DATA MANAGEMENT AND ANALYSIS

Course Objective

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

Learning Outcomes of the Course Unit

1   To be able to apply machine learning techniques in R and Python programs. 2   To make resistant machine learning predictions. 3   To know how to build machine learning models and combine them in solving any problem. 4   To know how to build machine learning models and combine them in solving any problem. 5   To be able to use data mining softwares.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Review of data preprocessing techniques 2 Support vector machine 3 Analysis in computer environment with support vector machine 4 Logic behind the kernel support vector machine and its application 5 Naive bayes and application 6 Regression, classification and application with decision trees 7 Regression, classification and application with random forest 8 k-means and hierarchical clustering and application 9 Deep learning and practice 10 Artificial neural networks and application 11 Model diversification with bagging 12 Boosting and XGBoost techniques and application 13 Case Studies 14 Case Studies

Recomended or Required Reading

1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, (2005). 2. Gökhan Silahtaroğlu, Kavram ve Algoritmalarıyla Temel Veri Madenciliği, Papatya Yayıncılık (2008)

Planned Learning Activities and Teaching Methods

1. Lecture Method 2. Implementation Method 3. Discussion method

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 * 0.20 + STT * 0.30 + FIN* 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.30 + RST* 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

The weighted average of the midterm grade, the midterm work and the final grade must be 75 and above.

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 Final 1 3 3 Midterm 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 1 LO.3 1 LO.4 1 LO.5 1 1