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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS CME 4403 INTRODUCTION TO MACHINE LEARNING ELECTIVE 2 2 0 6

Offered By

Computer Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ZERRIN IŞIK

Offered to

Computer Engineering

Course Objective

The aim of this course is to provide students with the theoretical basis of machine learning algorithms and practical application of them on real-world data sets.

Learning Outcomes of the Course Unit

1   Describe basic machine learning concepts 2   Solve a particular problem that includes one of the learning types 3   Apply machine learning techniques on given dataset 4   Develop a project with use of a machine learning approach 5   Evaluate a learning model

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 First View: Machine Learning 2 Data Insights, Descriptive Statistics 3 Decision Tree Learning 4 Nearest Neighbour Algorithm 5 Bayesian Learning 6 Regression Analysis 7 MIDTERM 8 Logistic Regression 9 Support Vector Machines 10 Model Evaluation Methods 11 Neural Networks 12 Multilayer Neural Networks 13 Unsupervised Learning 1 14 Unsupervised Learning 2

Recomended or Required Reading

TextBook: Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, second edition, 2010. Complementary Book: Tom Mitchell, Machine Learning, McGraw-Hill.

Planned Learning Activities and Teaching Methods

Lectures / Presentation Guided problem solving Lab exercises Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 PRJ PROJECT 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.20 + PRJ * 0.40 + FIN * 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + PRJ * 0.40 + RST * 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Learning outcome 1, 2 and 3 will be evaluated in exam. Learning outcomes 4 and 5 will be supported by project.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Asst.Prof.Dr. Zerrin IŞIK Dokuz Eylul University Department of Computer Engineering Tinaztepe Campus 35160 BUCA/IZMIR Tel: (232) 301 74 13 E-mail: zerrin@cs.deu.edu.tr

Office Hours

TBA

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 2 26 Tutorials 13 2 26 Preparations before/after weekly lectures 14 1 14 Preparation for midterm exam 1 17 17 Preparation for final exam 1 19 19 Preparing assignments 1 30 30 Final 1 3 3 Midterm 1 3 3 TOTAL WORKLOAD (hours) 138

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 4 2 2 LO.2 2 5 2 LO.3 4 3 1 3 5 LO.4 4 5 4 3 5 LO.5 2 2 3