COURSE UNIT TITLE

: INTRODUCTION TO MACHINE LEARNING

DEGREE PROGRAMMES

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 Ensemble Decision Trees
5 Nearest Neighbour Algorithm
6 Bayesian Learning
7 Regression Analysis
8 Logistic Regression
9 Support Vector Machines
10 Model Evaluation Methods
11 Introduction to Neural Networks
12 Backpropagation and Gradient Descent
13 Convolutional Neural Networks
14 Recurrent Neural Networks

Recomended or Required Reading

Text Book: Fundamentals of Machine Learning for Predictive Data Analytics, John Kelleher, Brian Mac Namee, Aoife D'Arcy, The MIT Press, 2020
Complementary Book: Introduction to Machine Learning, Ethem Alpaydin, The MIT Press, 2014

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)

Assoc.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 14 2 28
Tutorials 14 2 28
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 14 14
Preparation for final exam 1 14 14
Project Preparation 1 30 30
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 148

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1523
LO.23533
LO.3533333
LO.4355433
LO.53333

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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