MATLAB Live Webinar

Machine Learning Made Easy - Classification Learner Apps

Thursday, Feb 21st 2019, 3:00 pm 

Duration: 1 hour

Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.

Highlights include:

- Accessing, exploring, analyzing, and visualizing data in MATLAB

- Using the Classification Learner app to perform common machine learning tasks such as:

   o Feature selection and feature transformation

   o Specifying cross-validation schemes

   o Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, and discriminant analysis

   o Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your data

- Integrating trained models into applications such as computer vision, signal processing, and data analytics.

Speaker Profile

Watchara Toahngern
 

Watchara received his Master Engineering degree in Electrical and Information Engineering from the King Mongkut’s University of Technology Thonburi, Thailand. And He started his career at Trinergy Instrument working on the Software Developer position. There he has 5 Year experience for Automate Test & Measurement System Design.

 

After moving to Ascendas in 2018, Watchara joined the Application Engineering team and worked across multi-engineering disciplines. Watchara’ areas of Interested are in Physical Modelling, Machine Learning and Deep Learning, Image processing & Computer vision.

 
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