Machine Learning / 09
Probabilistic Learning
Work through priors, likelihood, posterior probability, and Naive Bayes methods.
This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.
Subtopics
- 09.01
Probability in ML
Planned guide: Probability in ML. Focus on intuition, assumptions, and a worked example.
- 09.02
Bayes' Theorem
Planned guide: Bayes' Theorem. Focus on intuition, assumptions, and a worked example.
- 09.03
Prior Probability
Planned guide: Prior Probability. Focus on intuition, assumptions, and a worked example.
- 09.04
Likelihood
Planned guide: Likelihood. Focus on intuition, assumptions, and a worked example.
- 09.05
Posterior Probability
Planned guide: Posterior Probability. Focus on intuition, assumptions, and a worked example.
- 09.06
Maximum Likelihood
Planned guide: Maximum Likelihood. Focus on intuition, assumptions, and a worked example.
- 09.07
Naive Bayes
Planned guide: Naive Bayes. Focus on intuition, assumptions, and a worked example.
- 09.08
Gaussian Naive Bayes
Planned guide: Gaussian Naive Bayes. Focus on intuition, assumptions, and a worked example.
- 09.09
Multinomial Naive Bayes
Planned guide: Multinomial Naive Bayes. Focus on intuition, assumptions, and a worked example.
- 09.10
Bernoulli Naive Bayes
Planned guide: Bernoulli Naive Bayes. Focus on intuition, assumptions, and a worked example.