A Markov decision process (MDP) is a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. It involves choosing actions in different states, which result in rewards and transitions to new states. MDPs are an extension of Markov chains, but with the addition of actions and rewards.

Stanford University

Winter 2023

This course provides a deeper understanding of robot autonomy principles, focusing on learning new skills and physical interaction with the environment and humans. It requires familiarity with programming, ROS, and basic robot autonomy techniques.

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+ 13 more conceptsPrinceton University

Spring 2019

This introductory course focuses on machine learning, probabilistic reasoning, and decision-making in uncertain environments. A blend of theory and practice, the course aims to answer how systems can learn from experience and manage real-world uncertainties.

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+ 21 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 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 conceptsUC Berkeley

Fall 2008

This advanced course focuses on the applications of machine learning in the robotics and control field. It covers a wide range of topics including Markov Decision Processes, control theories, estimation methodologies, and robotics principles. Recommended for graduate students.

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+ 27 more conceptsCarnegie Mellon University

Spring 2018

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.

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+ 55 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.

No concepts data

+ 24 more concepts