COURSE UNIT TITLE

: MACHINE LEARNING AND REASONING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5007 MACHINE LEARNING AND REASONING ELECTIVE 3 0 0 9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR RECEP ALP KUT

Offered to

Industrial Ph.D. Program In Advanced Biomedical Technologies
Industrial Ph.D. Program In Advanced Biomedical Technologies
Computer Engineering (Non-Thesis-Evening) (English)
Computer Engineering Non-Thesis (English)
Biomedical Tehnologies (English)
Computer Engineering (English)
Computer Engineering (English)
COMPUTER ENGINEERING (ENGLISH)

Course Objective

Machine learning algorithms play an important role in industrial applications, commercial data analysis and especially data mining applications. The aim of this course is to give students both the theoretical justification and practical application of machine learning algorithms on real-world data sets. This course is intended for graduate students who conduct research in fields which use machine learning, such as computer vision, natural language processing, data mining, bioinformatics, and robotics.

Learning Outcomes of the Course Unit

1   Define three styles of learning: Supervised, Reinforcement, and Unsupervised
2   Define basic learning techniques such as Decision Trees, Bayesian Learning, Neural Networks, Genetic Algorithms etc.
3   Determine which machine learning technique is appropriate to solve a particular problem
4   Design a machine learning model
5   Implement a simple machine learning algorithm

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Machine Learning
2 Basic concepts and issues in Machine Learning
3 Learning (Supervised, Unsupervised, Reinforcement)
4 Real world machine learning applications
5 Bayesian Learning: Bayes Theorem, Naive Bayes Classifier
6 Decision Tree Learning
7 Artificial Neural Networks
8 Genetic Algorithm
9 Instance Based Learning: K-Nearest Neighbor
10 Reinforcement Learning
11 Unsupervised Learning: Clustering
12 Self Organizing Maps
13 Presentations
14 Presentations

Recomended or Required Reading

Textbook(s): Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, first edition 2004, second edition 2010.

Supplementary Book(s): Igor Kononenko, Matjaz Kukar (2007) Machine Learning and Data Mining: Introduction to Principles and Algorithms, Horwood Publishing Limited, 454 pages.

Planned Learning Activities and Teaching Methods

Lectures,
Research,
Application Development,
Presentation,
Term project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PRS PRESENTATION
3 FCG FINAL COURSE GRADE ASG * 0.50 + PRS * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Course outcomes will be evaluated with the presentation of the student about a topic and project / report prepared by the student.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Prof.Dr. R. Alp KUT
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35160 BUCA/IZMIR
Tel: +90 (232) 301 74 01
e-mail: alp@cs.deu.edu.tr

Office Hours

Thursday 9:00 - 10:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 2 28
Project Preparation 1 65 65
Preparing presentations 1 50 50
Preparing report 1 40 40
TOTAL WORKLOAD (hours) 225

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1433
LO.2453
LO.351
LO.4412
LO.5421

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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