18CSE479T - Statistical Machine Learning UNIT 4 & 5 (12 MARKS)

 12M:


Back propagation:

Backpropagation algorithm: 

  • Inputs x arrive through preconnected path

  • The input is modeled using true weights W. the weights are usually chosen randomly

  • Calculate the output of each neuron from the input layer to the hidden layer to the output layer

  • Calculate the error in the outputs (Backpropagation error = actual output - desired output)

  • From the output layer, go back to the hidden layer to adjust the weights to reduce the error

  • Repeat the process until the desired output is achieved

Example:

First do forward pass and compute error…like this

Then based on error do back propagation

And write that this continues until target output is reached


K-Means clustering:

ACTUALLY THEY HAVE MENTIONED USING ELBOW METHOD, BUT YOU CAN’T DO IT MANUALLY…COZ IT IS COMPLICATED AND TAKES A LOT OF TIME…SO JUST DO WITHOUT ELBOW METHOD


Here is the link to a website where they explain the working out of the K-means algorithm

There are two problems, you can go through it and try to use the Euclidean distance formula because that is only mentioned in the ppt


Link: Example for K-Means Clustering


Code for Single Value Decomposition

Code for SVM:

This is too big. You can directly refer the ipynb file

Link: SVM

See till before RBF Grid Search

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