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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 ALPER VAHAPLAR

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 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 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

Lecture and class presentation

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 FIN FINAL EXAM 3 FCG FINAL COURSE GRADE MTE * 0.50 + FIN * 0.50 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.50 + RST * 0.50 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Mid-term exam: 50% Final Exam: 50%

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 3 36 Preparation for midterm exam 1 15 15 Preparation for final exam 1 25 25 Final 1 2 2 Midterm 1 2 2 TOTAL WORKLOAD (hours) 119

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 PO.11 PO.12 PO.13 LO.1 4 5 5 3 LO.2 4 4 5 LO.3 5 LO.4 5 4 3 5 LO.5 5 5 4 4