about this material
All about practicing perplexing probability, this content helps us to understand the world around us from games to economics to science.
Bayes' theorem - one of the most important formulas in all of probability - is central to scientific discovery, a core tool in machine learning, and (as some notable scientists refer to it) the geometry of changing beliefs.
Learning outcomes of this introductory content include:
1. Hold a beginner's understanding of Bayes' theorem;
2. Recognize and avoid common misconceptions; and
3. Apply the concepts of Bayes' theorem in practice of learning.
pre-learning
(10 minutes)
Asynchronous content to prepare learners:
- Explore Probability Fundamentals (Why does Bayes' theorem matter?)
micro-lesson 1
(10 minutes)
Synchronous content to invite learners to consider a first concept:
- Thinking Probabilistically (What is Bayes' theorem saying?)
micro-lesson 2
(10 minutes)
Synchronous content to invite learners to consider a second concept:
micro-lesson 3
(10 minutes)
Synchronous content to invite learners to consider a third concept:
- Probability is Everywhere (When is Bayes' theorem useful?)
follow-up activity
Learners apply the introductory concepts and consult mentors, instructors, and peers as they problem solve:
- Apply your learning
- Test Bayes' theorem model of probability using data sets that are relevant to your self-directed project. Show your work, teach a peer, ask questions!