Review By Anonymous7

[Review by Anoymous7] This slecture is about classification by using Bayes Rule in 1-dimensional and N-dimensional feature spaces. In the beginning, the author introduced Bayes theorem and gave 2 examples that use Bayes theorem. The author then discussed classification using Byes rule and derived the error formula for calculating the error when classifying 1 dimensional Gaussian distribution. After that, the author derived the discriminant function when classifying 1 dimensional and N dimensional Gaussian distribution.

[Review by Anoymouse7] Overall, the slecture is very well written. The flow in the slecture seems to be smooth. However, to make the slecture better, the following improvements are suggested:

• The dimensional classification error is not written correct. Because of the author’s choice of classes $ \omega_1 $ and $ \omega_2 $, the 1 dimensional classification error should be,

• In the Bayes rule example 1, it was written that P(W | L) = 0.75. It should be P(L | W) = 0.75 instead.

• When comparing the original probability and the probability that we get by applying Byes rule in examples 1 and 2, it should be explained why the probability changed.
• There should be a derivation of the discriminant function when it is the case of the general $ \Sigma_i $ in the N dimensional feature space.

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