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Course Information

Course Name
Turkish Yapay Öğrenme
English Machine Learning
Course Code
BLG 527E Credit Lecture
(hour/week)
Recitation
(hour/week)
Laboratory
(hour/week)
Semester 1
3 3 - -
Course Language English
Course Coordinator Yusuf Yaslan
Course Objectives This course will provide students an overview of issues, algorithms and techniques in machine learning. Students will also gain theoretical and practical experience through programming exercises and projects.
Course Description Introduction, Cross Validation; Supervised, Unsupervised and Semi-Supervised Learning; Feature and Sample Selection; Dimensionality Reduction; Clustering; Classifiers; Regressors; Ensemble Methods (Combining Multiple Learners); Decision Trees; Linear Dicrimination; Support Vector Machines; Multilayer Perceptrons; Parametric Methods; Multivariate Methods; Evaluation and Assessment Metrics/Criteria
Course Outcomes I. Ability to design a machine learning method for a specific problem
II. Ability to analyze performance of different machine learning methods
III. Ability to combine outputs of different machine learning methods
IV. Ability to understand the theoretical foundations and workings of different machine learning methods
Pre-requisite(s) 1. Ability to use a programming language such as java, python, C or Matlab. 2. Having a working knowledge of linear algebra and stats would help.
Required Facilities
Other Coursebook, computer for doing homeworks and following the course material.
Textbook ETHEM ALPAYDIN, “INTRODUCTION TO MACHINE LEARNING”, 3rd EDITION, THE MIT PRESS, 2014. https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/

Rogers, Simon, and Mark Girolami. A first course in machine learning. Chapman and Hall/CRC, 2016.
Other References CHRIS BISHOP, PATTERN RECOGNITION AND MACHINE LEARNING, SPRINGER 2006.

PATTERN CLASSIFICATION, 2ND EDITION, RICHARD O. DUDA, PETER E. HART, AND DAVID G. STORK, 2000, WILEY
 
 
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