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所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-algebra-theory-and-implementation/
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课程名称：掌握线性代数：理论与代码实现 课程概述： 线性代数是计算科学中最重要的数学分支之一，广泛应用于机器学习、人工智能、数据科学、统计学、模拟、计算机图形学等领域。本课程旨在传授应用线性代数的核心概念，而不仅仅是抽象的数学理论。课程将通过 MATLAB 和 Python 进行数学概念的实际应用，帮助学员理解在真实世界应用中如何有效使用线性代数。 课程特点： - 清晰易懂的线性代数概念与理论解释。 - 多种不同的解释方法，帮助深化理解。 - 通过图形、数字和空间的可视化增强几何直觉。 - 初学者到中级的主题，包括向量、矩阵乘法、最小二乘投影、特征分解和奇异值分解。 - 强调现代应用导向的线性代数和矩阵分析。 - 直观的讲解对角化、特征值和特征向量及奇异值分解。 - 提升编程技能，课程需要有一定的编码基础。 学习线性代数的好处： - 理解统计学及其包含的最小二乘法、回归和多变量分析。 - 改进工程、计算生物学、金融和物理的数学模拟。 - 理解数据压缩和降维技术（PCA、SVD、特征分解）。 - 理解机器学习及线性分类算法的数学基础。 - 深化信号处理方法的知识，特别是过滤和多维子空间方法。 - 探索线性代数、矩阵与几何之间的联系。 - 提高在 Python 和 MATLAB 中实现数学及理解机器学习概念的经验。 授课教师介绍： 讲师在研究和教学中广泛使用线性代数，已撰写多本有关数据分析、编程和统计学的教材，这些教材依赖于线性代数的概念。现在就观看课程介绍视频和免费样本视频，了解更多课程内容和教学风格。如果您对课程是否适合自己有疑问，欢迎在注册前与我联系。期待在课程中见到您！
You need to learn linear algebra!Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on.You need to know applied linear algebra, not just abstract linear algebra!The way linear algebra is presented in 30-year-old textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the "determinant" of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications? The answer may surprise you, and it's in this course!If you are interested in learning the mathematical concepts linear algebra and matrix analysis, but also want to apply those concepts to data analyses on computers (e.g., statistics or signal processing), then this course is for you! You'll see all the maths concepts implemented in MATLAB and in Python.Unique aspects of this courseClear and comprehensible explanations of concepts and theories in linear algebra.Several distinct explanations of the same ideas, which is a proven technique for learning.Visualization using graphs, numbers, and spaces that strengthens the geometric intuition of linear algebra.Implementations in MATLAB and Python. Com'on, in the real world, you never solve math problems by hand! You need to know how to implement math in software!Beginning to intermediate topics, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition.Strong focus on modern applications-oriented aspects of linear algebra and matrix analysis.Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition.Improve your coding skills! You do need to have a little bit of coding experience for this course (I do not teach elementary Python or MATLAB), but you will definitely improve your scientific and data analysis programming skills in this course. Everything is explained in MATLAB and in Python (mostly using numpy and matplotlib; also sympy and scipy and some other relevant toolboxes).Benefits of learning linear algebraUnderstand statistics including least-squares, regression, and multivariate analyses.Improve mathematical simulations in engineering, computational biology, finance, and physics.Understand data compression and dimension-reduction (PCA, SVD, eigendecomposition).Understand the math underlying machine learning and linear classification algorithms.Deeper knowledge of signal processing methods, particularly filtering and multivariate subspace methods.Explore the link between linear algebra, matrices, and geometry.Gain more experience implementing math and understanding machine-learning concepts in Python and MATLAB.Linear algebra is a prerequisite of machine learning and artificial intelligence (A.I.).Why I am qualified to teach this course:I have been using linear algebra extensively in my research and teaching (in MATLAB and Python) for many years. I have written several textbooks about data analysis, programming, and statistics, that rely extensively on concepts in linear algebra. So what are you waiting for??Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.I hope to see you soon in the course!Mike
