First Steps in Linear Algebra for Machine Learning

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课程大纲

Systems of linear equations and linear classifier
Full rank decomposition and systems of linear equations
Eucledian spaces
Final Project

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The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability. This course is suitable for you if you are not an absolute beginner in Matrix Analysis or Linear Algebra (for example, have studied it a long time ago, but now want to take the first steps in the direction of those aspects of Linear Algebra that are used in Machine Learning). Certainly, if you are highly motivated in study of Linear Algebra for Data Sciences this course could be suitable for you as well.

机器学习线性代数的第一步:本课程的主要目的是解释用于数据分析和机器学习的线性代数的主要概念。另一个目标是提高学生在机器学习和数据分析中使用线性代数方法的实践技能。您将学习处理矢量和矩阵形式的数据的基础知识,掌握解决线性代数方程组系统的知识,并找到基本的矩阵分解和对其适用性的一般理解。 如果您不是Matrix Analysis或Linear Algebra的绝对入门者(例如,很久以前已经研究过,但现在想朝着Linear Algebra那些方面的方向迈出第一步),则本课程适合您。用于机器学习)。当然,如果您对数据科学的线性代数学习充满热情,那么本课程也可能适合您。

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