{"id":1920,"date":"2019-05-18T12:00:00","date_gmt":"2019-05-18T10:00:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/machine-learning-questions-and-answers-question-1-to-10\/"},"modified":"2026-07-05T03:23:24","modified_gmt":"2026-07-05T01:23:24","slug":"machine-learning-questions-and-answers-question-1-to-10","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/machine-learning-questions-and-answers-question-1-to-10\/","title":{"rendered":"Machine Learning Questions and Answers \u2013 (Question 1 to 10)"},"content":{"rendered":"

I’m happy to the making this lesson. I would give you brief answers to several Machine Learning questions. But if you would like to go in-depth, then you can watch the video explanation of the answers.
\nYou can find Question 1 to 20 below<\/p>\n

Questions 1 to 10<\/a>.<\/p>\n

Question 11 to 20<\/a>.
\nSo let’s get started!<\/p>\n

 <\/p>\n

1. What is Maximum Likelihood Estimation(MLE)?<\/strong><\/h5>\n

Maximum Likelihood Estimation<\/a> is a procedure used to estimate an unknown parameter of a model. MLE\u00a0 is based on the Likelihood Function<\/a> and it works by making an estimate the maximizes the likelihood function.\u00a0 The likelihood function is simply a function of the unknown parameter, given the observations(or sample values).<\/p>\n

Therefore, maximum likelihood estimate is the value of the parameter that maximizes the likelihood of getting the the observed data.<\/p>\n

 <\/p>\n

2. Explain Decision Theory in Machine Learning?<\/strong><\/h5>\n

First, I would like to remind you that the three fundamental theories of machine learning<\/a> are<\/p>\n

Probability Theory, Information Theory and Decision Theory<\/a>.<\/p>\n

Now, decision theory\u00a0 in Machine Learning is the strategies and method involved in choosing a particular action among a number of probable actions.<\/p>\n

 <\/p>\n

3. What is Bayesian Model<\/strong><\/h5>\n

Bayesian Model<\/strong> is a probabilistic model (a system of making inference) that is based on Bayes’ Theorem<\/a>. The Bayesian model attempts to obtain a\u00a0 posterior distribution base on some prior distribution.<\/p>\n

For example, if we have the density function for some observations Xi<\/sub><\/em> for i = 1 to n to be f(Xi<\/sub> |\u00a0\u03b8)<\/strong><\/em> for unknown parameter\u00a0\u03b8.<\/p>\n

Then the prior distribution is given by p(\u03b8), Bayesian model would try to find the parameter using the posterior p(\u03b8 | X)<\/em><\/strong><\/p>\n

Just as a reminder, you can find Bayes’ Theorem<\/a> below:<\/p>\n

\"Final<\/p>\n

I recommend you watch the video explanation. You’ll understand it better.<\/p>\n