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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
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INDUSTRIAL ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Compulsory
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Course Delivery Method
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Face To Face
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Objectives of the Course
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Introducing supervised and unsupervised learning problems
Implementing statistical learning methods
Obtaining sufficient background to support further studies in data science
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Course Content
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INFORMS (The Institute for Operations Research and the Management Sciences) define business analytics as the scientific process of transforming data into insight for making better decisions. This course introduces essential analytic methods in descriptive, predictive and prescriptive business analytics, and can be thought of as a confluence of statistics, operations research, data mining, and machine learning. This course will emphasize machine learning. Machine learning uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications spanning from business intelligence to homeland security, from analyzing biochemical interactions to structural monitoring of aging bridges, and from emissions to astrophysics, etc. This class will familiarize students with a broad cross-section of models and algorithms for machine learning, and prepare students for research or industry application of machine learning techniques. The course includes computer implementations using available up-to-date software and programming languages.
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Course Methods and Techniques
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The course will be taught through theoretical explanations, sample problem solving, in-class discussions and practical exercises. In addition, group work and interactive learning techniques will be used to increase student participation. Homework and practical work with software tools will be done to reinforce the topics.
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Prerequisites and co-requisities
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( IE222 ) and ( IE212 )
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Course Coordinator
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Associate Prof.Dr. Ramazan Ünlü ramazan.unlu@agu.edu.tr
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Name of Lecturers
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Associate Prof.Dr. RAMAZAN ÜNLÜ
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Assistants
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Research Assist. Mehmet Eren Nalici mehmeteren.nalici@agu.edu.tr
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
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Alpaydın, Ethem. Introduction to Machine Learning. The MIT Press, 2014.
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Course Notes
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Will be shared on canvas
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Documents
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Canvasta paylaşılacaktır
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Assignments
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Canvasta paylaşılacaktır
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Exams
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Yüzyüze yapıalcaktır.
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Course Category
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Mathematics and Basic Sciences
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%40
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Engineering
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%40
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Field
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%20
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