Reinforcement learning is a type of machine learning that focuses on how an intelligent agent should take actions in an environment to maximize rewards. It differs from supervised learning by not needing labelled input/output pairs and does not require sub-optimal actions to be corrected. It is typically stated as a Markov decision process, allowing for dynamic programming techniques to be used.

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 conceptsStanford University

Winter 2023

This course offers a solid introduction to the field of reinforcement learning (RL), covering challenges, approaches, and deep RL. Prerequisites include Python proficiency and foundations of machine learning. Students will be able to implement RL algorithms and evaluate them.

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+ 11 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

Fall 2022-2023

Offered by Stanford University, this course focuses on AI applications in healthcare, exploring deep learning models for image, text, multimodal, and time-series data in the healthcare context. Topics also address AI integration challenges like interpretability and privacy.

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+ 27 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 conceptsCarnegie Mellon University

Spring 2020

This course provides a comprehensive introduction to deep learning, starting from foundational concepts and moving towards complex topics such as sequence-to-sequence models. Students gain hands-on experience with PyTorch and can fine-tune models through practical assignments. A basic understanding of calculus, linear algebra, and Python programming is required.

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+ 40 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.

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+ 24 more conceptsBrown University

Spring 2022

Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts.

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+ 40 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