Course Syllabus

Introduction to Machine Learning

ML using GoogleCloud AutoML and MATLAB

Dates:

Prerequisites:

Basic computer knowledge
Basic statistics background

Dr. Kee Moon is a professor in the Department of Mechanical Engineering at SDSU and a Co-Leader of the Research Capacity Core & Health Sensor Methods Group at the SDSU HealthLINK Center. Dr. Kee Moon’s primary research interests are in smart sensor and actuator technology, including the development of ultrasonic recharging technology for implantable medical devices as well as brain-computer-interface technology. At the SDSU HealthLINK Center, Dr. Kee Moon guides researchers on the development of portable, wearable health sensor technologies that can provide real-time health monitoring.

By the end of this course participants will be able to:

Describe common machine learning techniques
Understand the toolbox of available options in MATLAB for machine learning
Describe considerations regarding how to interpret and evaluate machine learning models
Apply cloud-based machine learning techniques using AutoML

This two-day (6-hour) workshop covers elementary machine learning techniques in MATLAB and AutoML, utilizing MATLAB Statistics, Machine Learning Toolbox, and Deep Learning Toolbox. Attendees will learn how to use unsupervised learning to uncover features in large datasets and supervised learning to develop prediction models through examples and in-class hands-on activities

Session 1 (3 hours, day 1)

Introduction to Machine Learning

Machine Learning paradigms
Supervised (classification, regression)
Unsupervised (clustering)
Reinforcement

Data Preparation & Model Building

Organizing and preprocessing data
Generating training and checking data
Building Machine Learning Models

Machine Learning Practice with AutoML

Creating classification and regression models
Interpreting and evaluating models
Clustering data
Interpreting and evaluating models

Session 2 (3 hours, day 2)

MATLAB with Machine Learning Toolbox

Machine Learning practice: repeat the same exercise as Session 1 using the same dataset, but with MATLAB.

Creating classification and regression models with MATLAB
Interpreting and evaluating models
Clustering data with MATLAB
Interpreting and evaluating models