Learn to Program: The Fundamentals (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/programming1

简介: Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.

C++ For C Programmers (CourseraArchive) 2 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

主页: https://www.coursera.org/course/cplusplus4c

简介: This course is for experienced C programmers who want to program in C++. The examples and exercises require a basic understanding of algorithms and object-oriented software.

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).

程序设计实习 / Practice on Programming (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/pkupop

简介: 《程序设计实习》课程是北京大学的本科生主干基础课程。课程的主要目标有二： 一、掌握C++语言的语法和标准模板库，能用C++语言以面向对象的方法来编写规模较大的程序。 二、掌握枚举、递归、搜索、动态规划等基本的算法思想。通过大量的高强度的编程训练，提高动手能力，做到能较为熟练、完整、准确地实现自己设计的程序，为进一步学习其他计算机专业课程，或在其他专业领域运用计算机编程解决问题奠定良好的基础。

Discrete Optimization (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/optimization

简介: Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization, including constraint programming, local search, and mixed-integer programming.