Unsupervised learning involves algorithms that learn patterns from unlabeled data, allowing for the discovery of concise representations of input data. This approach differs from supervised learning, where models are trained to map input to target output, and also differs from reinforcement learning and semi-supervised learning.

UC Berkeley

Spring 2020

The course addresses programming parallel computers to solve complex scientific and engineering problems. It covers an array of parallelization strategies for numerical simulation, data analysis, and machine learning, and provides experience with popular parallel programming tools.

No concepts data

+ 36 more conceptsStanford 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.

No concepts data

+ 32 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.

No concepts data

+ 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.

No concepts data

+ 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.

No concepts data

+ 55 more conceptsStanford University

Spring 2022

This is a deep-dive into the details of deep learning architectures for visual recognition tasks. The course provides students with the ability to implement, train their own neural networks and understand state-of-the-art computer vision research. It requires Python proficiency and familiarity with calculus, linear algebra, probability, and statistics.

No concepts data

+ 55 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.

No concepts data

+ 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.

No concepts data

+ 22 more concepts