Stanford University(斯坦福大学) 公开课(进行中)

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Giving 2.0: The MOOC (CourseraArchive) 0 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/giving2

简介: A philanthropist is anyone who gives anything — time, money, experience, skills, and networks — in any amount, to create a better world. This course will empower you to become a more effective philanthropist and make your giving more meaningful to both you and those you strive to help.

Mining Massive Datasets (CourseraArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: 7 weeks

主页: https://www.coursera.org/course/mmds

简介: This class teaches algorithms for extracting models and other information from very large amounts of data. The emphasis is on techniques that are efficient and that scale well.

EP101: Environmental Physiology: Your Body in the World (Stanford Online) 0 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/HumanitiesSciences/EP101/Environmental_Physiology/about

简介: Welcome to Environmental Physiology! We are excited that you want to explore your body's place in the world. Your body is an amazing machine that is equipped with the capacity to deal with the world's great stressors.

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).

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.

Introduction to Logic (CourseraArchive) 3 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/intrologic

简介: In this course, you will learn how to formalize information and reason systematically to produce logical conclusions. We will also examine logic technology and its applications - in mathematics, science, engineering, business, law, and so forth.

Compilers (CourseraArchive) 2 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/compilers

简介: This course will discuss the major ideas used today in the implementation of programming language compilers. You will learn how a program written in a high-level language designed for humans is systematically translated into a program written in low-level assembly more suited to machines!

Computer Security (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/security

简介: Learn how to design secure systems and write secure code.

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.

Algorithms: Design and Analysis, Part 1 (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/algo

简介: In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures (heaps, hash tables, search trees), randomized algorithms, and more.

Introduction to Mathematical Thinking (CourseraArchive) 2 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/maththink

简介: Learn how to think the way mathematicians do - a powerful cognitive process developed over thousands of years.