COURSE UNIT TITLE

: MACHINE LEARNING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIL 3112 MACHINE LEARNING ELECTIVE 3 0 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR METE EMINAĞAOĞLU

Offered to

Computer Science

Course Objective

The basic goal of this course is to provide the knowledge of the modern algorithms which are used in the supervised learning techniques. The field of machine learning is interested in how the self-improving programs can automatically be generated. During the learning process, the theoretical properties of the machine learning algorithms will be given and the application-based studies will be offered.

Learning Outcomes of the Course Unit

1   Have a good understanding of learning and reasoning strategy.
2   Have a good understanding of the theory of machine learning techniques.
3   Have a good ability to use the machine learning techniques.
4   Have ability to make use of the algorithmic solution techniques.
5   Have a good understanding of difference and similarity parts of the machine learning

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Basic concepts in machine learning
2 Standardization methodologies
3 Performance measurement and evaluation for classification (Sensitivity, Specificity, ErrorRate, Confusion Matrix)
4 Classification algorithms: Decision Trees (ID3)
5 Decision Trees (C4.5)
6 Bayes Theorem and Naive Bayes Classifier (1st Quiz)
7 Similarity and distance measures
8 Mid-term exam
9 k Nearest Neighbors Algorithm (k-NN)
10 Artificial Neural Networks - Part 1
11 Artificial Neural Networks - Part 2
12 Regression Analysis - Part 1 (2nd Quiz)
13 Regression Analysis - Part 2
14 General review

Recomended or Required Reading

Textbook(s): Tom M. Mitchell, Machine Learning, McGraw Hill, 1997.
Supplementary Book(s): Pierre Baldi, Søren Brunak, Bioinformatics: The Machine Learning Approach, The MIT Press, 2001.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

Successful / Unsuccessful


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Mid-term exam: 30%
Quiz: 30%
Final Exam: 40%

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

cagin.kandemir@deu.edu.tr

Office Hours

will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 12 2 24
Preparation for midterm exam 1 10 10
Preparation for final exam 1 20 20
Preparation for quiz etc. 2 5 10
Preparing assignments 4 5 20
Final 1 2 2
Midterm 1 2 2
Quiz etc. 2 0,5 1
TOTAL WORKLOAD (hours) 128

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.14553
LO.2445
LO.35
LO.45435
LO.55544

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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