2022 COSS Machine Learning

This interactive online workshop is an introduction to the world of machine learning (ML), covering some supervised learning algorithms and when and how to use them. 

It begins by introducing the data pipeline and its processes, before moving on to statistical and visualization approaches to conduct exploratory and descriptive analytics on data in answering the question “What happened in the past?”. From there, participants will explore the art of data preparation, including data cleaning, missing values, outlier detection, and feature transformation and engineering.

Next, we will introduce predictive analytics to answer the question “What will happen?”. We will cover techniques for classifying and predicting data for the supervised learning algorithm, such as k-NN, Naïve Bayes, Decision Tree and Random Forest, and provide guidance in deciding which ones to use. Finally, participants will learn about statistical evaluation methods used in comparing the performance of predictive modelling techniques.

This workshop balances theory and practice. Participants will use practical concepts of machine learning applications to understand real-world situations. 

Topics 

• Data preparation
• Machine learning theory
• Machine learning process
• Machine learning algorithms 
• Model evaluation

Course Prerequisites: Participants are expected to be familiar with the programming language, python, and have a basic understanding of data preparation. 

This workshop will be delivered in 3 sessions:

• Session 1: Monday, June 13, 2022 from 9:00 AM - 12:00 PM EDT
  • Data Preparation Theory & Demo
• Session 2: Wednesday, June 15, 2022 from 9:00 AM - 12:00 PM EDT     
  • Modelling Theory
• Session 3: Friday, June 17, 2022 from 9:00 AM - 12:00 PM EDT
  • Modelling Practice 

Registration opens: May 30, 2022 at 12:00 PM EDT.