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Neural Networks for Machine Learning (CourseraArchive) 5 个评论 关注 开始时间: 04/22/2022 持续时间: 8 weeks 主页: https://www.coursera.org/course/neuralnets 简介: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. |
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Learning From Data (EdxArchive) 1 个评论 关注 开始时间: 04/22/2022 持续时间: 10 weeks 主页: https://www.edx.org/archive/learning-data-caltechx-cs1156x 简介: Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing." |
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Computer Vision: The Fundamentals (CourseraArchive) 0 个评论 关注 开始时间: 04/22/2022 持续时间: 未知 主页: https://www.coursera.org/course/vision 简介: In this course, we will study the concepts and algorithms behind some of the remarkable successes of computer vision - capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities and more. |
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Probabilistic Graphical Models (CourseraArchive) 5 个评论 关注 开始时间: 04/22/2022 持续时间: 11 weeks 主页: https://www.coursera.org/course/pgm 简介: In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques. |
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Machine Learning (CourseraArchive) 29 个评论 关注 开始时间: 04/22/2022 持续时间: Unknown 主页: https://www.coursera.org/course/ml 简介: Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. |
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CVX101: Convex Optimization (Stanford Online) 1 个评论 关注 开始时间: 04/22/2022 持续时间: 未知 主页: https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/about 简介: This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance. |
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Natural Language Processing (CourseraArchive) 7 个评论 关注 开始时间: 04/22/2022 持续时间: 10 weeks 主页: https://www.coursera.org/course/nlangp 简介: Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing. |
