How Machines Learn
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Objective
Students will understand the general concepts of how a machine/computer can learn using pattern recognition and math
Part 1 of 4
Warm-up video
TED-Ed · 4:57Vetted channel
We don't generate video — this one is by TED-Ed on YouTube. The practice questions and exit ticket below were drafted by AI for this objective, and every question is editable in the teacher guide.
Part 2 of 4
Key concepts
3 concepts
- 1
The three basic types of machine learning are unsupervised learning, supervised learning, and reinforcement learning.
- 2
Unsupervised learning is useful for finding general similarities and patterns, while supervised learning requires active input from doctors and computer scientists to improve accuracy.
- 3
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.
Part 3 of 4
Practice
8 questions
What is the primary difference between supervised and unsupervised learning?
Which type of machine learning is best suited for recommending treatment plans that adapt over time based on patient response?
Part 4 of 4
Exit ticket
Quick comprehension check
“Describe one of the three types of machine learning (unsupervised, supervised, or reinforcement) discussed in the video, explaining how it uses pattern recognition and math to learn.”
Sample answer included in the free materials
Get the materials
Teacher guide, student handout, and slides — free, in Google Docs format
Teacher Guide
Complete lesson plan with answer keys and alternate activities
Student Handout
Printable worksheet
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