Course Details Language of Instruction English Level of Course Unit Bachelor's Degree Department / Program INDUSTRIAL ENGINEERING Type of Program Formal Education Type of Course Unit Compulsory Course Delivery Method Face To Face Objectives of the Course Introducing supervised and unsupervised learning problems Implementing statistical learning methods Obtaining sufficient background to support further studies in data science Course Content 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. Course Methods and Techniques In response to the developing situation with earthquake, our course will be offered in an online format. For asynchronous sessions CANVAS and for synchronous sessions Zoom will be used. We will be using various tools for active learning to take place. This is also a student-driven course. It is your responsibility to participate actively in class discussions. You are not graded on whether you agree or disagree with the instructor or with each other. Evaluation of class participation will be based on your ability to raise and answer important issues, to contribute ideas or insights, to build upon the ideas of others, ask questions to presenters, etc. By actively participating in the class discussions, you can sharpen your insights, and those of your classmates. Both the quality and frequency of your participation will count towards your active participation grade. Please note that high-quality or relevant contribution will earn you a higher participation grade than frequent but insignificant contribution. Also, you will not get any class participation points for just being present in class. Class attendance is a necessary but not a sufficient condition for scoring highly on the class participation. Prerequisites and co-requisities ( IE222 ) and ( IE212 ) Course Coordinator Associate Prof.Dr. Ramazan Ünlü ramazan.unlu@agu.edu.tr Name of Lecturers Associate Prof.Dr. RAMAZAN ÜNLÜ Assistants Research Assist. Mehmet Eren Nalici mehmeteren.nalici@agu.edu.tr Work Placement(s) No Recommended or Required Reading Resources Alpaydın, Ethem. Introduction to Machine Learning. The MIT Press, 2014. Will be shared on canvas Canvasta paylaşılacaktır Canvasta paylaşılacaktır Yüzyüze yapıalcaktır. Course Category Mathematics and Basic Sciences %40 Engineering %40 Field %20

 





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