Pattern Recognition Training Courses
Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data.
Pattern Recognition Course Outlines
|patternmatching||Pattern Matching||14 hours||Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not. Audience Engineers and developers seeking to develop machine vision applications Manufacturing engineers, technicians and managers Format of the course This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision. Introduction Computer Vision Machine Vision Pattern Matching vs Pattern Recognition Alignment Features of the target object Points of reference on the object Determining position Determining orientation Gauging Setting tolerance levels Measuring lengths, diameters, angles, and other dimensions Rejecting a component Inspection Detecting flaws Adjusting the system Closing remarks|
|datamodeling||Pattern Recognition||35 hours||This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience Data analysts PhD students, researchers and practitioners Introduction Probability theory, model selection, decision and information theory Probability distributions Linear models for regression and classification Neural networks Kernel methods Sparse kernel machines Graphical models Mixture models and EM Approximate inference Sampling methods Continuous latent variables Sequential data Combining models|
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