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Design of Computer Programs (Udacity) 4 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/cs212 简介: Learn new concepts, patterns, and methods that will expand your programming abilities, helping move you from a novice to an expert programmer. |
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機器學習基石 (Machine Learning Foundations) (CourseraArchive) 10 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/ntumlone 简介: Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。] |
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Web Development (Udacity) 3 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/cs253 简介: Starting from the basics of how the web works, this class will walk you through everything you need to know to build your own blog application and scale it to support large numbers of users. |
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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. |
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斯坦福大学公开课：编程范式 (网易公开课) 0 个评论 关注 开始时间: 04/22/2022 持续时间: 斯坦福大学公开课：编程范式 主页: http://v.163.com/special/opencourse/paradigms.html 简介: 该课程主讲C和C++高级内存管理特色；命令式和面向对象2种范式的差异。函数范式（LISP）和并行编程（C和C++） Python C#等新语言概论。基础要求:具备编程能力，能在抽象化层次上解决问题。学术应该有一定的C++基础。熟悉矩阵、指针、引用、类、算法、递归、链表、HASH算法、迭代等。 |
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Introduction to Recommender Systems (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/recsys 简介: This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems! |
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Game Theory (CourseraArchive) 1 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/gametheory 简介: The course covers the basics: representing games and strategies, the extensive form (which computer scientists call game trees), repeated and stochastic games, coalitional games, and Bayesian games (modeling things like auctions). |
