Lecture II - Bayesian and Maximum Likelihood Analyses
The goal for this lecture is to familiarize the students with some applications of Bayes theorems and of Maximum Likelihood Estimation. In particular:
Sept 5, 2017 - 12:15-13:10
- Naive Bayes Classifier
- Language Detection
- Central Limit Theorem
- Maximum Likelihood Estimation
- Binomial Distribution
- Beta Distribution
- A/B Testing
- p-values
- Bonferoni Correction
- Simpson’s Paradox
By the end of the lecture the students will understand some of the fundamental concepts and caveats involved in using Naive Bayesian analysis and Maximum Likelihood Estiamation.