# A new Machine Learning course

I have been learning about machine learning these days, and a good course I found is from 极客时间 机器学习 40 讲 https://time.geekbang.org/column/article/9762. The first three articles are about frequentist vs bayesian.

In short, frequentist views parameters as fixed, but the data is limited in size, so we need to estimate the parameter using noisy data.

The disadvantage of frequentist, is that when the data size is too small, we cannot estimate the parameters reliably.

In contrast, bayesian sees data as given, and the parameters follow a certain distribution, so we need to estimate the random variable of parameter from the fixed data.

The disadvantage of bayesian, is that the prior of parameter is hard to obtain, and it is subjective. We can use uninformative prior, such as a uniform distribution.

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