Please leave me comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further.

Questions and Comments

• SCarver Review: Your write-up is excellent. I had to nitpick little concerns in order to produce any feedback worthy comments. I feel like this slecture is a great supplement to my notes from the class so far, and have provided a few additional comments below:

• SCarver Comment 1: Your example to introduce Bayes' Theorem and the statistics behind it was fantastic. Even though you listed the Axioms of Probability as required knowledge, I fully understood what and why you covered the material, and I do not have a large background in this topic.

• SCarver Comment 2: The observation/explanation/inference section was great to explain the link from grinding out probabilities to using Bayes' Theorem. However, the Venn Diagram that was also shown was a bit misleading. While it was correct, I feel it should have been a diagram relating back to the example as well, instead of a generic partition with As and B.

• SCarver Comment 3: From your 3 learning goals at the beginning, I expected 3 sections detailing the parts of the slecture. However, the classification problem and Bayes' Rule sections were combined with very little being mentioned about the nature of a classification problem aside from how to approach and apply Bayes' Theorem to it.

• SCarver Question: In your final section before the conclusion, you state that the Bayes' Classifier is a classification method that uses Bayes' Rule, and the method you showed made sense. Is there any additional information that can be gained by performing these calculations in the way you showed in Eq.4 vs Eq.3, or is it simply preferred to use Eq.4 due to it matching up with the available data for real-world situations?

• Additional Questions / Comments

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