This document discusses using Matlab for machine learning tasks like classification. It covers loading and preparing a dataset, using various classification algorithms like decision trees and KNN, evaluating model performance with metrics like confusion matrices and ROC curves. It also discusses using the neural network toolbox to create and test a neural network classifier, training it on a labeled dataset and then using the trained model to classify new unlabeled data.

1. Machine Learning (ML)
using Matlab
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2. 2
Dataset
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Guardar archivo
en formato CSV

3. 3
Dataset
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csvwrite("data.csv",M);

4. 4
Dataset
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Input: features Output: label

5. Classification
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6. 6
ML Apps
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We select
to work
with the
workspace
data

7. 7
Classification
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By default
the decision
trees
algorithm
appears,
but we can
select other
algorithms

8. 8
Classification
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Select all
available
algorithms
Run the
training
process of
the
algorithms

9. Select one
with the
best
accuracy
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Classification
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Selecting
the KNN
algorithm

10. 10
Classification
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Plot of
features
Confusion
matrix
usage to
evaluate
the quality
of the
output of a
classifier

11. 11
Classification
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Receiver
Operating
Characteris
tic curve,
or ROC
curve.
Export the
model to
.mat code

12. 12
Classification
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Save the script
as
trainClassifier.m

13. 13
Testing the classification algorithm
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The classification
algorithm must
receive the dataset
labeled to train.

14. 14
Testing the classification algorithm
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15. 15
Testing the classification algorithm
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Dataset different
from the training
data, this dataset
has no labels.

16. 16
Testing the classification algorithm
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17. 17
Testing the classification algorithm
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18. Neural Network (NN)
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19. 19
Neural Network ToolBox
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We will use the
pattern
recognition app
Assigned probability
values per column
(Column 1 - label 1,
Column 2 - label 2, ...
Column n - label n).

20. 20
Neural Network ToolBox
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Select Next

21. 21
Neural Network ToolBox
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Select the Min and Mout matrices of the workspace.
Then select Next.

22. 22
Neural Network ToolBox
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Select Next
Select the
values according
to the dataset.

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Neural Network ToolBox
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Select Next
Select the number of
neurons according to the
application.

24. 24
Neural Network ToolBox
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Select train

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Neural Network ToolBox
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Select Next

26. 26
Neural Network ToolBox
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Select Next

27. 27
Neural Network ToolBox
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Export the neural network as a function

28. 28
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Save the script as
myNeuralNetworkFunction.m
Neural Network ToolBox

29. 29
Testing NN algorithm
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The function
receives the data
without label.
Probability of belonging to each
label (column)

30. 30
Testing NN algorithm
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Each Ynn row:
maximum
probability value,
cluster id.

31. 31
Testing NN algorithm
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Testing NN algorithm
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Testing NN algorithm
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Recursos:
• Presentaciones, ejercicios resueltos y archivos VHDL:
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