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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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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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Machine Learning (CourseraArchive) 7 个评论 关注 开始时间: 04/22/2022 持续时间: Unknown 主页: https://www.coursera.org/course/machlearning 简介: Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy! |
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Algorithms, Part II (CourseraArchive) 5 个评论 关注 开始时间: 04/22/2022 持续时间: 6 weeks 主页: https://www.coursera.org/course/algs4partII 简介: This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. |
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Computational Methods for Data Analysis (CourseraArchive) 1 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/compmethods 简介: Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences.
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StatLearning: Statistical Learning (Stanford Online) 3 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/HumanitiesScience/StatLearning/Winter2014/about 简介: This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). |
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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. |
