Module Outline
Module 1
Summary
In this module, we introduce you to the unique features of Practicum AI; visual, story-driven learning backed by hands-on activities. And because the best way to learn something is through practice, you’ll develop an AI project plan. In it, you’ll present a research question, discuss the kinds of data needed to answer that question, and finish off with the steps needed to complete the project, including the selection and training of a model. But don’t worry! Everything you need to know about the AI project life-cycle is presented here.
Objectives
By the end of this module, students will be able to:
- Summarize the unique learning features of the Practicum AI training program.
- Classify the different kinds of data and the suitable deep learning techniques for each.
- List the four kinds of deep learning systems and explain the unique application of each.
- Outline the lifecycle steps of an AI project and the importance of each.
- Develop an AI project plan.
To Do List
- Create an AI project plan for a project of interest to you.
Read
A Brief History of Artificial Intelligence by Michael Wooldridge.
Watch
- Practicum Introduction
- The Critical Importance of Data
- An Overview of Deep Learning Systems
Complete
Optional Content / Additional Resources
See Deep Learning: A Visual Approach, chapter 1 for additional content about the different kinds of AI systems.
Module 2
Summary
The history of artificial intelligence is important. It helps to know how others have thought about and defined intelligence. It also allows us to appreciate the different ways in which scientists have sought to make machines intelligent. These can be divided into two broad categories: top-down and bottom-up. In this module, both top-down and bottom-up approaches to AI are covered, including the famous Turing Test. This historical knowledge, in turn, allows us to see how AI has developed over time - the rise of machine learning followed by a renewed interest in neural networks.
Objectives
By the end of this module, students will be able to:
- Compose a short definition of artificial intelligence.
- Describe the basic difference between machine learning and deep learning.
- Summarize the Turing Test and discuss its significance in AI.
- Contrast top-down (knowledge representation) approaches to AI with bottom-up (data driven) approaches.
- Provide concrete examples of how AI is used in at least one area of interest.
To Do List
Read
A Brief History of Artificial Intelligence by Michael Wooldridge.
Watch
- Artificial Intelligence Defined
- Top-Down and Bottom-Up AI - A Historical View
Complete
Optional Content / Additional Resources
See Deep Learning: A Visual Approach, chapter 1 for additional content about the different kinds of AI systems.
