Free One Hour Short Courses

Free One Hour Short Courses

These courses are asynchronous, meaning you can sign up any time throughout the year.

Snapshot of AI

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Description & Objectives

This course first provides an introduction to Artificial Intelligence and explores the importance of the technology. It then covers machine learning and the types of machine learning.

By the end of this course, students will be able to:

  • Define artificial intelligence.
  • Explain the importance of artificial intelligence and why we should care about it.
  • Explore the concept of machine learning and its different types and applications.

Snapshot of AI for STEM Learners

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Description & Objectives

This course is asynchronous, meaning you can sign up any time throughout the year.

This course provides the framework for identifying and applying AI systems for real-world applications.  By the end of the course, you will have a basic understanding of all the components of an AI system.

By the end of this course, students will be able to:

  • Identify different types of learning in Machine Learning.
  • Identify different types of intelligence in Artificial Intelligence.
  • Identify different components of an AI system.

Intro to AI Applications

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Description & Objectives

This course aims to provide an iterative framework to develop real-world machine learning systems that learn from data, reason with data, and are deployed, reliable, and scalable. The focus of this course is to introduce basic modules of machine learning systems, namely, data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics.

By the end of this course, students will be able to:

  • Define basic terminology for Artificial Intelligence (AI), Machine Learning (ML),
  • and Deep Learning (DL) tools. Identify basic principles of data collection.
  • Identify basic principles of data and feature engineering.
  • Select the correct model for a particular task or application.