2. Agenda
■ Rules and Attendance
■ Lab
– Lab Configuration
– Lab Tools
– Lab Experiment
■ Tutorial
– Solve Sheet 1 “ Problem 2 and 4”
■ 15 Minutes In Deep
– Estimating Probabilities
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3. 1. Rules and Attendance
■ Tutorial Starts 11:00 - Ends 1:10
■ I will start at 11:05
■ Attendance at 11:08
■ Allowance Until 11:10
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5. 2. Lab Configuration – Experiment
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Dataset
Training
Dataset
Testing
Dataset
75%
25%
Pattern
Recognition
Algorithm
Model
X(Feature Vector)
Calculate
Error
Y
(Real Class)
Accuracy
Y
(Predicted Class)
6. 2. Lab Configuration – Algorithm
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Pattern
Recognition
Algorithm
As Example
Naive Bayes Classifier
Assign x to W2 if :
Given
• X: given data
• W1,W2 Two classes
7. 2. Lab Configuration – Gaussian Bayes Classifier
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Unknown
• Mean of Data
• Standard Deviation
Loss Matrix Probability of Classes
Given
Ex: Gaussian
Unknown
Calculated from your dataset
8. 2. Lab Configuration – Bayes Experiment
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Dataset
Training
Dataset
Testing
Dataset
75%
25%
Bayes Classifier
1. Probability
2. Expectation
3. Standard
Deviation
Model
X(Feature Vector)
Calculate
Error
Y
(Real Class)
Accuracy
Y
(Predicted Class)
9. 2.2. Lab Tools
1. Select Dataset from UCI Machine Learning Repo
– https://archive.ics.uci.edu/ml/index.html
2. Install Matlab with PR Toolbox or Python with Scikit-learn
– PR Toolbox :
■ https://drive.google.com/drive/folders/0B9lOqlIVVRRIOF9VVFBtNHU4bEk?usp=drive_web
– Scikit Learn :
■ http://scikit-learn.org/
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We will tell you What to do
But you have to know How to do it