Probability is a measure of how likely an event is to occur, expressed as a number between 0 and 1 or 0% and 100%. Complementary events have probabilities that add up to 1. Probability theory is used to draw inferences about the expected frequency of events in areas such as statistics, mathematics, science, finance, etc.

Stanford University

Winter 2023

This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.

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+ 32 more conceptsUC Berkeley

Fall 2022

UC Berkeley's CS 188 course covers the basic ideas and techniques for designing intelligent computer systems, emphasizing statistical and decision-theoretic modeling. By the course's end, students will have built autonomous agents that can make efficient decisions in a variety of settings.

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+ 20 more conceptsCarnegie Mellon University

Spring 2019

This course from Carnegie Mellon University provides a deep understanding of AI's theory and practice, covering methods for decision-making, problem-solving, and handling uncertainty. Topics include search algorithms, computational game theory, and AI ethics.

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+ 24 more conceptsStanford University

Winter 2023

The course introduces decision making under uncertainty from a computational perspective, covering dynamic programming, reinforcement learning, and more. Prerequisites include basic probability and fluency in a high-level programming language.

No concepts data

+ 22 more concepts