Hidden Markov Models are statistical models used to describe a system with unobservable states. They require an observable process whose outcomes are influenced by the hidden states, and that the outcomes of the hidden and observable states at any given time must be conditionally independent. HMM's have many applications in various fields such as thermodynamics, economics, signal processing, and bioinformatics.
Stanford 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 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 conceptsBrown University
Fall 2022
CS1410 at Brown University delves into the realm of Artificial Intelligence. Using the 3rd edition of "Artificial Intelligence, A Modern Approach" by Russell & Norvig, students explore intelligent agents, game theory, knowledge representation, logic, probabilistic learning, NLP, robotics, computer vision, and ethical implications of AI.
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+ 22 more concepts