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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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Introduction to Hadoop and MapReduce (Udacity) 1 个评论 关注 开始时间: 04/22/2022 持续时间: 自主 主页: https://www.udacity.com/course/ud617 简介: The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing. Learn the fundamental principles behind it, and how you can use its power to make sense of your Big Data. |
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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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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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Natural Language Processing (CourseraArchive) 3 个评论 关注 开始时间: 04/22/2022 持续时间: Unknown 主页: https://www.coursera.org/course/nlp 简介: In this class, you will learn fundamental algorithms and mathematical models for processing natural language, and how these can be used to solve practical problems. |
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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. |
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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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Social and Economic Networks: Models and Analysis (CourseraArchive) 3 个评论 关注 开始时间: 04/22/2022 持续时间: 9 weeks 主页: https://www.coursera.org/course/networksonline 简介: Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions. |
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Statistical Mechanics: Algorithms and Computations (CourseraArchive) 0 个评论 关注 开始时间: 04/22/2022 持续时间: Unknown 主页: https://www.coursera.org/course/smac 简介: In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach. |
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人群与网络 People & Networks (CourseraArchive) 0 个评论 关注 开始时间: 04/22/2022 持续时间: 15 weeks 主页: https://www.coursera.org/course/peopleandnetworks 简介: 学习运用计算思维分析社会学、经济学问题的方法，加深对某些生活现象的理解，体会计算与社会科学的互动。 Learn to analyze and reason about problems in social sciences with computational thinking, appreciate interactions between computing and social sciences, as well as gain deeper understanding of some common phenomena in life and society |
