2022 ISDSA Meeting

A Gentle Introduction to Machine Learning

June 2, 2022: 8:00AM-11:00AM (US Eastern Time)
By Professor Karl Ho

This workshop is designed to introduce the fundamentals and practical applications of modeling data using data mining and machine learning methods.  It surveys a series of topics starting from exploratory data analysis, unsupervised learning, supervised learning and other learning methods to uncover the patterns and structure of data targeting practical problem solving and identifying solutions.  Students will learn by hands-on programming including such methods as regression, classification, logistic models, non-linear models, tree-based models, association rules, neural network and support vector machines.  New developments in learning such as model visualization, deep learning and interpretable machine learning will also be introduced and illustrated.

Learning Objectives:

At completion of this course, students will be able to:

1. Start familiarizing with data mining and machine learning methods
2. Design programs for modeling data applying machine learning techniques
3. Interpret data analytics for problem solving and decision making

This program is designed for hands-on experience without prerequisites.  Some experience in statistical concepts and data programming will help but more importantly students curious with the data science journey and applications will be a plus. The course includes lectures focused on the scope and applications of data science, followed by hands-on modules training students in data programming. Students are expected to bring own device (i.e. PC or Mac computers) and run programs either on cloud platforms or locally installation applications.  Sample programs and instruction materials will be provided and students are recommended to install software ahead of class per instructions.

Pre-class: Introduction of software and cloud computing

• All software/applications used in this class are open-sourced
• Programming in cloud platforms (RStudio cloud)
• Recommended accounts: GitHub

Module 1: Introduction: What’s Machine Learning for?

• Brief history
• Concepts: Graphical illustrations
• Applications: how to use machine learning for problem solving?

Module 2: Unsupervised Learning

• What is unsupervised learning?
• Benefits of pattern recognition
• Hands-on: Cluster analysis, Topic modeling using text data

Module 3: Supervised Learning

• What is supervised learning?
• Types of learning models
• Hands-on: Regression, classification and tree-based models

Conclusion: New developments and methods

• More methods in ML
• Model Visualization
• Deep learning
• Hands-on: Interpreted Machine Learning