MKM 602E - Machine Learning Applications in Mechatronic

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.