Conditional probability is a measure of the probability of an event occurring, given that another event has already occurred. It is denoted as P(A|B) and represents the probability of event A happening under the condition that event B has occurred. Conditional probabilities can be used to analyze the relationship between events and can be reversed using Bayes' theorem or displayed in a conditional probability table.

UC Berkeley

Fall 2022

UC Berkeley's course blends inferential thinking, computational thinking, and real-world relevance, offering students hands-on analysis of real-world datasets. It covers critical concepts in computer programming, statistical inference, privacy, and study design.

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+ 33 more conceptsUniversity of Washington

Winter 2022

This course dives deep into the role of probability in the realm of computer science, exploring applications such as algorithms, systems, data analysis, machine learning, and more. Prerequisites include CSE 311, MATH 126, and a grasp of calculus, linear algebra, set theory, and basic proof techniques. Concepts covered range from discrete probability to hypothesis testing and bootstrapping.

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+ 41 more conceptsUC Berkeley

Fall 2022

CS 70 presents key ideas from discrete mathematics and probability theory with emphasis on their application in Electrical Engineering and Computer Sciences. It addresses a variety of topics such as logic, induction, modular arithmetic, and probability. Sophomore mathematical maturity and programming experience equivalent to an Advanced Placement Computer Science A exam are prerequisites.

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+ 32 more conceptsBrown University

Spring 2023

CSCI 0220 provides a foundation in discrete math and probability theory. Key topics include logic, set theory, number theory, combinatorics, graph theory, and probability. No prior math background assumed. Aims to develop problem solving, communication, and collaboration skills. Introduces new concepts and ways of thinking to enable analyzing problems arising in computer science. Beginner-friendly introduction to core mathematical concepts underlying many aspects of CS.

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+ 26 more conceptsStanford University

Spring 2023

This course offers a thorough understanding of probability theory and its applications in data analysis and machine learning. Prerequisites include CS103, CS106B, and Math 51 or equivalent courses.

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+ 24 more concepts