MATLAB机器学习培训与咨询

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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