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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-ml-methods-in-petroleum-industry-seminar/
课程评论：没有评论
课程名称：石油行业中的机器学习（ML）方法研讨会 概述：本研讨会由资深石油工程数据顾问主讲，涵盖了机器学习概念及其在油气行业中的应用。课程以机器学习的定义开始，强调其从数据中学习的能力，而无需显式编程。研讨会指出了机器学习应用的广泛领域，包括图像和语音识别、欺诈检测和金融预测。课程大纲包括以下内容：机器学习简介、描述性统计、回归分析、分类、聚类和时间序列预测。 课程核心内容集中在关键的机器学习技术上：监督学习、无监督学习和强化学习。监督学习部分详细介绍了线性回归、逻辑回归、支持向量机（SVM）、决策树和集成技术等方法。无监督学习则强调了K均值聚类、层次聚类和维度缩减等技术。此外，特征工程和特征选择被讨论为机器学习工作流程中的关键步骤，涉及从现有数据中创建新特征以及识别对模型构建最相关的特征。 描述性统计被呈现为理解数据的基础，使用P值和相关系数来确定变量之间的显著性和关系。研讨会上还概述了数据类型，分为定性（属性）和定量（分类）。回归分析占据了相当大的篇幅，包括线性回归、多元线性回归和非线性回归模型，并强调了在石油行业中的具体应用，如地震解释、储层表征和PVT建模等。 最后，研讨会还涵盖了时间序列预测，使用统计、机器学习和深度学习方法进行分析。课程谈到统计方法，如移动平均，以及更先进的机器学习方法，如随机森林，最后介绍了递归神经网络等深度学习技术。
This seminar, presented by Sr. Petroleum Engineering Data Consultant, which covers a broad overview of machine learning concepts and their application within the oil and gas sector. It starts with the definition of machine learning (ML), emphasizing its ability to learn from data without explicit programming. The presentation highlights the wide range of ML applications, from image and speech recognition to fraud detection and financial forecasting, with following agenda: Introduction to Machine LearningDescriptive StatisticsRegressionClassificationClusteringTime Series forecastingThe core of the presentation focuses on key ML techniques: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is detailed with methods like linear regression, logistic regression, support vector machines (SVM), decision trees, and ensemble techniques. Unsupervised learning is highlighted with K-Means clustering, hierarchical clustering, and dimensionality reduction. Feature engineering and selection are discussed as critical steps in the ML workflow, involving the creation of new features from existing data and the identification of the most relevant features for model building.Descriptive statistics are presented as essential for understanding data, using P-values and correlation coefficients to determine significance and relationships between variables. The presentation outlines data types as qualitative (attributes) and quantitative (categorical). A significant portion is dedicated to regression analysis, including linear, multiple linear, and non-linear regression models. Specific applications in the petroleum industry are highlighted, which are including seismic interpretation, reservoir characterization, PVT modelling, etc.Finally, the presentation covers time series forecasting using statistical, machine learning, and deep learning methods. Statistical methods such as Moving Average are talked about with more advanced Machine Learning Methods such as Random Forest, ending with Deep Learning techniques like Recurrent Neural Networks.
