728x90 반응형 코세라72 Probability & Statistics for Machine Learning & Data Science (4) ▤ 목차Introduction 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 calcula.. 2024. 9. 6. Probability & Statistics for Machine Learning & Data Science (3) ▤ 목차Introduction 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.. 2024. 9. 5. Probability & Statistics for Machine Learning & Data Science (2) ▤ 목차Introduction 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.. 2024. 9. 4. Probability & Statistics for Machine Learning & Data Science (1) ▤ 목차Introduction 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 ran.. 2024. 9. 3. Probability & Statistics for Machine Learning & Data Science (0) ▤ 목차What you'll learnDescribe and quantify the uncertainty inherent in predictions made by machine learning modelsVisually and intuitively understand the properties of commonly used probability distributions in machine learning and data scienceApply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problemsAssess the per.. 2024. 9. 2. Calculus for Machine Learning and Data Science (11) Optimization in Neural Networks and Newton’s MethodNewton’s MethodNewton's MethodNewton’s method is an alternative to the gradient descent.In principle, Newton’s method is used to find the zeros of a function (where f(x) = 0).This is simply the formula of subtracting the previous point $x$ from the slope we calculate.Since Newton’s method is to find a zero of a function (f), it behaves like the .. 2024. 9. 1. 이전 1 ··· 4 5 6 7 8 9 10 ··· 12 다음 728x90 반응형
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