此为期两天的课程重点介绍 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  


课程要求
MATLAB 基础课程


Day 1 of 2


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


Day 2 of 2


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

等....课程


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