Variance is a measure of dispersion in probability theory and statistics, representing how far a set of numbers is spread out from their average value. It can be defined as the squared deviation from the mean or as the square of the standard deviation. There are two types of variance: one is part of a theoretical probability distribution, while the other is calculated from observations in a real-world system.

University 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 conceptsStanford University

Autumn 2022-2023

Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability.

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+ 88 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