Calculus and Optimization for Machine Learning

所在平台: CourseraArchive

课程类别: 其他类别

大学或机构: CourseraNew

课程主页: https://www.coursera.org/archive/calculus-and-optimization-for-machine-learning

课程评论:没有评论

第一个写评论        关注课程

课程大纲

Introduction: Numerical Sets, Functions, Limits
Limits and Multivariate Functions
Derivatives and Linear Approximations: Singlevariate Functions
Derivatives and Linear Approximations: Multivariate Functions
Integrals: Anti-derivative, Area under Curve
Optimization: Directional derivative, Extrema and Gradient Descent

课程评论(0条)

课程详情

Hi! Our course aims to provide necessary background in Calculus sufficient for up-following Data Science courses. Course starts with basic introduction to concepts concerning functional mappings. Later students are assumed to study limits (in case of sequences, single- and multivariate functions), differentiability (once again starting from single variable up to multiple cases), integration, thus sequentially building up a base for the basic optimisation. To provide an understanding of the practical skills set being taught, the course introduces the final programming project considering the usage of optimisation routine in machine learning. Additional materials provided during the course include interactive plots in GeoGebra environment used during lectures, bonus reading materials with more general methods and more complicated basis for discussed themes.

机器学习的微积分和优化:嗨!我们的课程旨在为微积分的后续学习提供足够的必要背景知识。 课程从基本介绍有关功能映射的概念开始。假定后来的学生学习极限(在序列,单变量和多元函数的情况下),可微性(再次从单变量开始到多个案例),整合,因此依次为基本优化奠定基础。为了提供对所教授的实践技能的理解,本课程介绍了最终编程项目,其中考虑了在机器学习中使用优化例程的情况。 本课程中提供的其他材料包括演讲期间使用的GeoGebra环境中的交互式图,带有更多通用方法和用于讨论主题的更复杂基础的有偿阅读材料。

课程标签

0人关注该课程

主题相关的课程