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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mathematics-for-machine-learning-o/
课程评论：没有评论
**课程名称：** 机器学习数学基础 **课程概述：** 本课程旨在为学习者提供机器学习算法所需的扎实的数学基础，从第一原理出发，帮助学生理解并实现各种机器学习技术。课程内容涵盖了以下几个核心数学领域： * **线性代数：** 重点讲解向量、矩阵、特征值和奇异值分解（SVD）等概念，这些是数据表示和转换的关键。 * **多变量微积分：** 深入学习梯度、雅可比矩阵和海森矩阵，这些是模型训练中优化技术的基础。 * **概率与统计：** 介绍随机变量、概率分布、期望、方差以及基本的统计推断方法。 * **优化方法：** 探讨梯度下降及其相关算法，理解机器学习模型如何从数据中学习。 课程强调数学证明和推导，旨在培养学习者解决问题的能力。通过**NumPy**和**SciPy**等强大的Python库，学生将获得编程实践经验，将数学概念转化为可执行的代码。课程的重点在于构建数学基础，而非应用模型于真实数据集。 **学习目标：** 完成本课程后，学习者将具备从头开始推导和实现机器学习技术所需的数学和计算工具，为深入学习人工智能、数据科学及高级数学建模打下坚实的基础。
This course provides a comprehensive foundation in the mathematical concepts essential for understanding and implementing machine learning algorithms from first principles. Students will explore Linear Algebra, covering vectors, matrices, eigenvalues, and singular value decomposition-critical for data representation and transformations. Multivariable Calculus will focus on gradients, Jacobians, and Hessians, which are fundamental to optimization techniques used in training models.The course also introduces Probability and Statistics, covering key topics such as random variables, probability distributions, expectation, variance, and fundamental statistical inference techniques. Optimization methods, including gradient descent and related algorithms, will be explored to understand how machine learning models learn from data. Additionally, students will develop problem-solving skills by working through mathematical proofs and derivations that underpin these techniques.Throughout the course, students will gain hands-on experience with NumPy and SciPy, leveraging these powerful Python libraries to implement mathematical concepts programmatically. Rather than applying models to real-world datasets, the focus will be on understanding and building the mathematical foundations necessary for machine learning. By the end of the course, students will have the necessary mathematical and computational tools to derive and implement machine learning techniques from scratch, preparing them for deeper study in artificial intelligence and data science, as well as advanced mathematical modeling.
