《机器学习：贝叶斯与优化视角》2015年新作详解

机器学习
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"《机器学习：贝叶斯与优化视角》（Machine Learning: A Bayesian and Optimization Perspective, 2015）是一本备受好评的学术著作，由塞尔吉奥·泰奥多里迪斯撰写。该书在亚马逊上获得了5颗星的高评价，表明其内容深受读者认可。本书旨在提供一种结合贝叶斯方法和优化理论的机器学习新视角，这在当时是机器学习领域的重要进展。 书中涵盖了机器学习的基本原理和方法，特别是强调了贝叶斯统计在处理不确定性、模型选择和推理过程中的核心作用。作者将这些概念与优化技术相结合，帮助读者理解如何通过优化算法寻找最佳解决方案，尤其是在数据挖掘和复杂决策问题中。这种综合的方法论使得本书不仅适合机器学习专业人员，也对那些希望深入了解这一领域的研究人员和工程师具有很高的价值。 书中的每一章都可能深入探讨了如下的关键知识点： 1. 贝叶斯方法：介绍了贝叶斯定理，包括贝叶斯网络、朴素贝叶斯分类器和马尔可夫链蒙特卡洛(MCMC)等，这些都是构建概率模型的基础工具。 2. 优化理论：涵盖了梯度下降、遗传算法、粒子群优化等经典优化算法，以及它们在机器学习中的应用，如参数调优和模型选择。 3. 概率与统计学习：如何利用概率统计分析数据，如似然函数、最大后验估计和贝叶斯推断，以提高模型的预测能力和解释性。 4. 模型评估与选择：讨论了模型复杂度控制、交叉验证和信息准则等方法，帮助读者选择最合适的模型。 5. 实际应用案例：书中可能包含一系列实际的机器学习项目，展示了如何运用贝叶斯和优化思想解决实际问题，如推荐系统、图像识别或自然语言处理任务。 6. 前沿研究：随着机器学习领域的快速发展，书中可能还涉及了一些当时的研究热点，如深度学习中的Bayesian深度学习和优化方法在神经网络训练中的应用。 《机器学习：贝叶斯与优化视角》是一本极具实用性和理论深度的书籍，对于想要掌握机器学习核心原理和技术的读者来说，是一本不可多得的参考资料。通过这本书，读者不仅能理解传统机器学习方法，还能了解如何将其与更先进的统计和优化思想融合，从而提升在当今数据驱动的世界中的竞争力。"

Linear.Algebra.and.Learning.from.Data_machinglearning_data_linea

2021-10-04 上传
Linear Algebra and Learning from data by Gilbert Strange pdf

Basics of Linear Algebra for Machine Learning

2018-12-30 上传
Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more. This book was designed to be a crash course in linear algebra for machine learning practitioners. Ideally, those with a background as a developer. This book was designed around major data structures, operations, and techniques in linear algebra that are directly relevant to machine learning algorithms. There are a lot of things you could learn about linear algebra, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials. I designed the tutorials to focus on how to get things done with linear algebra. They give you the tools to both rapidly understand and apply each technique or operation. Each tutorial is designed to take you about one hour to read through and complete, excluding the extensions and further reading. You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.

[machine_learning_mastery系列]Basics for Linear Algebra for Machine Learning.pdf

2018-06-05 上传
Preface I wrote this book to help machine learning practitioners, like you, get on top of linear algebra, fast. Linear Algebra Is Important in Machine Learning There is no doubt that linear algebra is important in machine learning. Linear algebra is the mathematics of data. It’s all vectors and matrices of numbers. Modern statistics is described using the notation of linear algebra and modern statistical methods harness the tools of linear algebra. Modern machine learning methods are described the same way, using the notations and tools drawn directly from linear algebra. Even some classical methods used in the field, such as linear regression via linear least squares and singular-value decomposition, are linear algebra methods, and other methods, such as principal component analysis, were born from the marriage of linear algebra and statistics. To read and understand machine learning, you must be able to read and understand linear algebra. Practitioners Study Linear Algebra Too Early If you ask how to get started in machine learning, you will very likely be told to start with linear algebra. We know that knowledge of linear algebra is critically important, but it does not have to be the place to start. Learning linear algebra first, then calculus, probability, statistics, and eventually machine learning theory is a long and slow bottom-up path. A better fit for developers is to start with systematic procedures that get results, and work back to the deeper understanding of theory, using working results as a context. I call this the top-down or results-first approach to machine learning, and linear algebra is not the first step, but perhaps the second or third. Practitioners Study Too Much Linear Algebra When practitioners do circle back to study linear algebra, they learn far more of the field than is required for or relevant to machine learning. Linear algebra is a large field of study that has tendrils into engineering, physics and quantum physics. There are also theorems and derivations for nearly everything, most of which will not help you get better skill from or a deeper understanding of your machine learning model. Only a specific subset of linear algebra is required, though you can always go deeper once you have the basics.

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There are several methods for changepoint detection, including statistical methods, machine learning techniques, and Bayesian methods. Statistical methods rely on the assumption that the data follows ...

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