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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mathematical-foundation-for-machine-learning-and-ai/
课程评论：没有评论
Coursera 课程《机器学习与人工智能的数学基础》旨在帮助学习者掌握人工智能和机器学习（AI/ML）领域所需的关键数学知识。 课程概述： 随着人工智能在各行各业的飞速发展，从自动驾驶到医疗诊断，AI/ML 的重要性日益凸显。然而，开发 AI/ML 程序需要扎实的编程和数学基础。本课程由行业专家设计，旨在将复杂的数学概念转化为易于理解的内容，为学习者构建坚实的数学基石。 课程内容涵盖三大核心数学理论： 1. **线性代数 (Linear Algebra)**： * 在机器学习中用于描述算法的参数和结构，是理解神经网络工作原理的关键。 * 涵盖标量、向量、矩阵、张量、矩阵范数、特殊矩阵与向量、特征值与特征向量等主题。 2. **多元微积分 (Multivariate Calculus)**： * 用于机器学习的学习过程，包括从示例中学习、更新模型参数和提升性能。 * 涵盖导数、积分、梯度、微分算子和凸优化等主题。 3. **概率论 (Probability Theory)**： * 用于在设计 AI/ML 算法时对底层数据做出假设，理解关键概率分布至关重要。 * 涵盖概率元素、随机变量、分布、方差与期望、特殊随机变量等主题。 课程特色： 每阶段都设有项目和测验，以巩固学习内容并展示实际应用。完成本课程后，学习者将不仅具备构建 AI/ML 算法的知识，更有信心将其应用于实际项目中，成为新一代的 AI 专家。
Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with the self-driving cars, medical diagnosis and even betting humans at strategy games like Go and Chess. The future for AI is extremely promising and it isn't far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn't easy and requires extensive programming and mathematical knowledge. Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this course, we've covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML. The course has been designed in collaboration with industry experts to help you breakdown the difficult mathematical concepts known to man into easier to understand concepts. The course covers three main mathematical theories: Linear Algebra, Multivariate Calculus and Probability Theory. Linear Algebra - Linear algebra notation is used in Machine Learning to describe the parameters and structure of different machine learning algorithms. This makes linear algebra a necessity to understand how neural networks are put together and how they are operating. It covers topics such as: Scalars, Vectors, Matrices, Tensors Matrix Norms Special Matrices and Vectors Eigenvalues and Eigenvectors Multivariate Calculus - This is used to supplement the learning part of machine learning. It is what is used to learn from examples, update the parameters of different models and improve the performance. It covers topics such as: Derivatives Integrals Gradients Differential Operators Convex Optimization Probability Theory - The theories are used to make assumptions about the underlying data when we are designing these deep learning or AI algorithms. It is important for us to understand the key probability distributions, and we will cover it in depth in this course. It covers topics such as: Elements of Probability Random Variables Distributions Variance and Expectation Special Random Variables The course also includes projects and quizzes after each section to help solidify your knowledge of the topic as well as learn exactly how to use the concepts in real life. At the end of this course, you will not have not only the knowledge to build your own algorithms, but also the confidence to actually start putting your algorithms to use in your next projects. Enroll now and become the next AI master with this fundamentals course!
