How Machines Learn
Learning Objective
Students will understand the general concepts of how a machine/computer can learn using pattern recognition and math
Key Concepts
The three basic types of machine learning are unsupervised learning, supervised learning, and reinforcement learning.
Unsupervised learning is useful for finding general similarities and patterns, while supervised learning requires active input from doctors and computer scientists to improve accuracy.
Reinforcement learning uses an iterative approach to gather feedback and create optimal plans, and artificial neural networks can use millions of connections to tackle difficult tasks.
Practice Questions
This lesson includes 8 practice questions to reinforce learning.
View questions preview
1. What is the primary difference between supervised and unsupervised learning?
2. Which type of machine learning is best suited for recommending treatment plans that adapt over time based on patient response?
3. Describe a scenario where unsupervised learning could be applied in a hospital setting.
...and 5 more questions
Educational Video
How does artificial intelligence learn? - Briana Brownell
TED-Ed