Phitcha Phitchayanon is an Application Engineer at Ascendas Systems (TechSource Systems), with a focus on Machine Learning and Deep Learning. Prior to this, Phitcha was a researcher at Research Centre for Carbon Solution (RCCS), UK, where contributed to multiple standardization projects for novel reactor design. He has been a research intern at Department of Petroleum and Chemical Engineering, Sultan Qaboos University, Oman. Phitcha has a M.Sc. in Energy Engineering from Heriot-Watt University, Scotland where his focus areas of study included Artificial Intelligence and low-cost controller and a B.Eng. in Chemical Engineering from King Mongkut’s University of Technology North Bangkok, Thailand.
Dujtep Thawonsusin is an Application Engineer at Ascendas Systems (TechSource Systems), with a focus on Signal Processing, Computer vision, Machine Learning, and Deep Learning. Prior to this, Dujtep published a paper “Human Fall Detection using Maximum Euclidean Distance and Ellipse Approximation” Int'l Journal of Advances in Mechanical & Automobile Engineer.
Dujtep graduated M.Eng. in Control System and Instrumentation engineer from King Mongkut’s University of Technology Thonburi, where his focus areas of study included Artificial Intelligence and Machine Vision and a B.Eng. in Control System and Instrumentation engineer from King Mongkut’s University of Technology Thonburi.
Are you new to Machine Learning and Deep Learning, and want to learn how to apply these techniques in your work?
Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to learn information directly from data without relying on a predetermined equation as a model. And deep learning is a machine learning method that applies neural networks with many hidden layers.
In this hands-on workshop, you will write code and use MATLAB to develop and optimize a classifier using Machine Learning.
1. Learn the fundamentals of machine learning and understand terms like “supervised learning”, “feature extraction”, and “hyperparameter tuning”
2. Build and evaluate machine learning model classifying human activity based on signals
3. Deploy Machine Learning Algorithm
In this seminar, we also introduce you to MATLAB Online and will give you tips and tricks of how to find materials/ scripts for your references and what’s new in MATLAB 2019b.
09.00 – 09.30
09.30 – 10.15
Introduction to Machine Learning and Regression App
10.15 – 10.30
15 minutes break
10.30 – 12.00
Machine Learning and Classification App
12.00 – 13.00
13.00 – 13.45
Feature extraction and feature selection
13.45 – 15.00
Workshop - Classification App
15.00 – 15.15
15 minutes break
15.15 – 16.00
MATLAB: Tips and Tricks