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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-sports-analytics
课程评论：没有评论
课程名称：体育分析中的机器学习入门 概述：本课程旨在让学生探索监督机器学习技术，使用Python的scikit-learn (sklearn) 工具包和真实的体育数据，以理解机器学习算法及预测运动结果。学生将在先前课程的基础上，应用如支持向量机（SVM）、决策树、随机森林、线性回归、逻辑回归及集成学习等方法，分析来自职业体育联赛（如NHL和MLB）和可穿戴设备（如Apple Watch和惯性测量单元IMUs）的数据。通过本课程的学习，学生将全面了解如何利用分类和回归技术推动体育分析在各种运动活动和赛事中的应用。 课程大纲： 1. 机器学习概念 - 描述：本周将介绍机器学习的概念，并描述其在体育分析中可以使用的四个主要领域。课程将讨论机器学习流程以及在体育分析中常见的问题。 2. 支持向量机 - 描述：本周学生将学习支持向量机（SVM）的工作原理，并通过棒球和可穿戴数据来体验这些模型。结束本周后，学生将具备使用真实数据构建SVM的经验，并能够将其应用于自己的问题。 3. 决策树 - 描述：本周将重点介绍可解释的机器学习方法，特别关注决策树。学生将学习这些模型的整体工作原理，并观察决策树与回归方法的特定应用。通过本周的学习，学生将更好地理解如何利用Python的sklearn工具包处理各种监督学习任务。 4. 集成与更多 - 描述：本周，学生将学习如何通过集成方法将许多不同的模型结合使用，随机森林方法是一个常见的例子，还将学习sklearn中可用的更一般方法，如堆叠和装袋。到本周结束，学生将对如何将SVM、决策树和逻辑回归等方法结合起来以提高性能有一个广泛的理解。
Name:Machine Learning Concepts
Description:This week will introduce the concept of machine learning and describe the four major areas of places it can be used in sports analytics. The machine learning pipeline will be discussed, as well as some common issues one runs into when using machine learning for sports analytics.
Name:Support Vector Machines
Description:In this week students will learn how Support Vector Machines (SVM) work, and will experience these models when looking at both baseball and wearable data. Coming out of the week students will have experience building SVMs with real data and will be able to apply them to problems of their own.
Name:Decision Trees
Description:This week will focus on interpretable methods for machine learning with a particular focus on decision trees. Students will learn how these models work in general, and see special uses of decision trees in combination with regression methods. In this week students will come to better understand how the python sklearn toolkit can be used for a breadth of supervised learning tasks.
Name:Ensembles & Beyond
Description: In this week of the course students will learn how many different models can be used together through ensembles, including the random forest method as a common use, as well as more general methods available in sklearn such as stacking and bagging. By the end of this week students will have a broad understanding of how methods such as SVMs, decision trees, and logistic regression can be used together to solve a problem with increasing performance.
In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs). By the end of the course students will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.
