此课程重点介绍 MATLAB 中使用 Statistics Toolbox , Machine Learning Toolbox™ 和
Deep Learning Toolbox™ 功能的数据分析和机器学习技术。本课程
演示如何通过非监督学习发现大数据集的特点，以及通过监督学
习建立预测模型。课程中的示例和练习强调用于呈现和评估结果
的技巧。内容包括：
- 组织和预处理数据
- 聚类数据
- 创建分类模型
- 评估和改善模型
- 化简数据集
- 改善模型性能
- 详细课程提纲：
-
课程要求
MATLAB 基础
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Importing and Organizing Data
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Objective: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.
· Data types
· Tables
· Categorical data
· Data preparation
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Finding Natural Patterns in Data
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Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.
· Unsupervised learning
· Clustering methods
· Cluster evaluation and interpretation
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Building Classification Models
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Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.
· Supervised learning
· Training and validation
· Classification methods
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Improving Predictive Models
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Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models.
· Cross validation
· Hyperparameter optimization
· Feature transformation
· Feature selection
· Ensemble learning
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Building Regression Models
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Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.
· Parametric regression methods
· Nonparametric regression methods
· Evaluation of regression models
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Creating Neural Networks
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Objective: Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.
· Clustering with Self-Organizing Maps
· Classification with feed-forward networks
· Regression with feed-forward networks
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