Course Information

Course Name	Turkish	Mekatronikte Yapay Öğrenme Uygulamaları
	English	Machine Learning Applications in Mechatronic
Course Code	MKM 602E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	-	-	-
Course Language		English
Course Coordinator		Hülya Yalçın
Course Objectives		To teach fundamentals of machine learning concepts and algorithms, To teach students programming techniques for machine learning and to encourage them to incorporate these techniques into their own research.
Course Description		Throughout this course, machine learning and statistical pattern recognition algorithms particularly for Mechatronic Engineering will be analyzed. Topics include: introduction to machine learning; probability, statistics and linear algebra recall; supervised learning (generative/discriminative learning, parametric/non-parametric learning, support vector machines); Bayes learning; unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); decision trees; hidden Markov models; linear classification methods; reinforcement learning. The course will also discuss recent applications of machine learning, such as learning robot applications, autonomous robot and vehicle navigation , machine learning of robot assembly plans and machine learning algorithms in image processing/machine vision techniques.
Course Outcomes		Students who successfully complete this course will obtain fundamental information and ability to 1. propose various machine learning techniques to solve problems specific to mechatronics area, 2. analyze performance of machine learning techniques, 3. fuse the results of various machine learning techniques 4. understand practical and theoretical aspects of machine learning algorithms
Pre-requisite(s)
Required Facilities
Other
Textbook		ETHEM ALPAYDIN, “INTRODUCTION TO MACHINE LEARNING”, THE MIT PRESS, 2004
Other References

Courses

Help

About