Coursera 썸네일형 리스트형 Probability & Statistics for Machine Learning & Data Science (8) Introduction to Probability and Probability DistributionsProbability DistributionsUniform DistributionUniform distribution has all possible values in an interval with the same frequency of occurrence.All intervals have the same probability.It has two parameters:a: beginning of an intervalb: end of the intervalNormal DistributionNormal distribution (aka Gaussian distribution) is a distribution th.. 더보기 Probability & Statistics for Machine Learning & Data Science (7) Introduction to Probability and Probability DistributionsProbability DistributionsProbability Distributions (Continuous)In the discrete distributions, the events always form a list of elements.In the continuous distributions, the events are an interval.What is the probability that you will wait exactly one minute for the call?더보기0Correct. Too many values for the amount of time a call can takePro.. 더보기 Probability & Statistics for Machine Learning & Data Science (6) Introduction to Probability and Probability DistributionsProbability DistributionsRandom VariablesRandom variables are the variables that can make many numbers.With the coin example, $X$ can take either 1 or 0, not just a single number.Which of the following are examples of discrete random variables? Select all that apply.Selecting a card from a deckDrawing a marble from a bag of colored marbles.. 더보기 Probability & Statistics for Machine Learning & Data Science (5) Introduction to Probability and Probability DistributionsQuizQ1You flip a fair coin two times. What is the probability of getting one head and one tail in any order?1/41/23/4Answer더보기2There are 4 possible outcomes (HH, HT, TH, or TT) when flipping a coin two times and there are two ways of getting one head and one tail, so the probability is 2/4 = 1/2Q2You throw two dice and sum the result, what.. 더보기 Probability & Statistics for Machine Learning & Data Science (4) Introduction to Probability and Probability DistributionsIntroduction to ProbabilityBayes Theorem - Spam exampleUsing Bayes’ theorem deletes (doesn’t use) all the unnecessary information and only uses the information that matters.In this example, we worry about emails that contain the word lottery and calculate the probability that are spam.A prior is the original probability that we calculate w.. 더보기 Probability & Statistics for Machine Learning & Data Science (3) Introduction to Probability and Probability DistributionsIntroduction to ProbabilityConditional Probability - Part 1 Conditional probability is all about calculating the probability of an event happening given that another event has already happened.What is the probability of landing heads twice if the first coin flip is heads?더보기1/4Correct! Given the first coin is head, to get two heads you nee.. 더보기 Probability & Statistics for Machine Learning & Data Science (2) Introduction to Probability and Probability DistributionsIntroduction to ProbabilityA sum of Probabilities (Disjoint Events)We can add the probabilities for disjoint events to obtain the result (a probability of a union).Disjoint means they cannot occur simultaneously (no overlapping). What is the probability that a kid plays soccer or basketball? 더보기0.7 When throwing a 6-sided dice, what is the.. 더보기 Probability & Statistics for Machine Learning & Data Science (1) Introduction to Probability and Probability DistributionsIn this week, you will learn about the probability of events and various rules of probability to correctly do arithmetic with probabilities. You will learn the concept of conditional probability and the key idea behind Bayes’ theorem. In lesson 2, we generalize the concept of probability of events to a probability distribution over random .. 더보기 이전 1 2 3 4 5 6 7 8 9 다음
