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

  Importing and Organizing Data	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
  Finding Natural Patterns in Data	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
  Building Classification Models	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

  Improving Predictive Models	Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models. · Cross validation · Hyperparameter optimization · Feature transformation · Feature selection · Ensemble learning
  Building Regression Models	Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables. · Parametric regression methods · Nonparametric regression methods · Evaluation of regression models
  Creating Neural Networks	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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