Desktop Basics - MATLAB & Simulink

Desktop Basics

When you start MATLAB®, the desktop appears in its default layout.

desktop basic

 

The desktop includes these panels:

  • Current Folder — Access your files.

  • Command Window — Enter commands at the command line, indicated by the prompt (>>).

  • Workspace — Explore data that you create or import from files.

As you work in MATLAB, you issue commands that create variables and call functions. For example, create a variable named a by typing this statement at the command line:

a = 1

MATLAB adds variable a to the workspace and displays the result in the Command Window.

a = 

     1

Create a few more variables.

b = 2
b = 

     2
c = a + b
c = 

     3
d = cos(a)
d = 

    0.5403

When you do not specify an output variable, MATLAB uses the variable ans, short for answer, to store the results of your calculation.

sin(a)
ans =

    0.8415

If you end a statement with a semicolon, MATLAB performs the computation, but suppresses the display of output in the Command Window.

e = a*b;

You can recall previous commands by pressing the up- and down-arrow keys, ↑ and ↓. Press the arrow keys either at an empty command line or after you type the first few characters of a command. For example, to recall the command b = 2, type b, and then press the up-arrow key.

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Machine Learning in MATLAB

Train Classification Models in Classification Learner App

Train Regression Models in Regression Learner App

Distribution Plots

Explore the Random Number Generation UI

Design of Experiments

Machine Learning Models

Logistic regression

Logistic regression create generalized linear regression model - MATLAB fitglm 2

Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification 2

Support Vector Machines for Binary Classification 3

Support Vector Machines for Binary Classification 4

Support Vector Machines for Binary Classification 5

Assess Neural Network Classifier Performance

Naive Bayes Classification

ClassificationTree class

Discriminant Analysis Classification

Ensemble classifier

ClassificationTree class 2

Train Generalized Additive Model for Binary Classification

Train Generalized Additive Model for Binary Classification 2

Classification Using Nearest Neighbors

Classification Using Nearest Neighbors 2

Classification Using Nearest Neighbors 3

Classification Using Nearest Neighbors 4

Classification Using Nearest Neighbors 5

Linear Regression

Linear Regression 2

Linear Regression 3

Linear Regression 4

Nonlinear Regression

Nonlinear Regression 2

Visualizing Multivariate Data

Generalized Linear Models

Generalized Linear Models 2

RegressionTree class

RegressionTree class 2

Neural networks

Gaussian Process Regression Models

Gaussian Process Regression Models 2

Understanding Support Vector Machine Regression

Understanding Support Vector Machine Regression 2

RegressionEnsemble