曙海培训-MATLAB机器学习培训与咨询-中心|上海南京苏州宁波深圳广州杭州北京

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MATLAB机器学习培训与咨询

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
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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

MATLAB机器学习培训与咨询-中心

课程介绍

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MATLAB机器学习培训与咨询

课程要求