This contains my submissions for the Soft Computing Elective Course at IIITA.

1. Python3
2. Numpy
3. Matplotlib
4. Matlab(Only for last assignment)

The assignment folders contain the description of the assignment statement along with the jupyter notebooks (and dataset).

1. Perform Linear Regression on the given dataset without regularization.

2. Perform Linear Regression on the given dataset with regularization. Also implement LWR and find out what happens when the value of tau is very small.

3. Using the data set of two examination results design a predictor using logistic regression for predicting

   1. Whether a student can get an admission in the institution.
   2. Whether the microchip will be accepted or not. Use regularizer to further tune the parameters. Use 70 % data for training and rest 30% data for testing your predictor and calculate the efficiency of the predictor/hypothesis. Use batch gradient descent.

4. Using the data set of two examination results design a predictor using logistic regression for predicting whether a student can get an admission in the institution. Use regularizer to further tune the parameters. Use 70 % data for training and rest 30% data for testing your predictor and calculate the efficiency of the predictor/hypothesis. This should be done with delta learning rule using Newton’s method and compare the results with using gradient descent.

5. Using Naive Baysian Classifier:

   1. Predict where a given mail is spam or not. Use the
      data set provided for this purpose. (structured data set)
   2. Using Naive Bayesian classifier predict river non river using Satellite data set of Hooghly river (unstructured data set).

6. Perform Face Recognition:

   1. Using PCA : Create face dataset using your mobile phone for your face as well as faces of 9 other friends. Create multiple variants (at least 5) of each faces with different view angles.
   2. Using LDA : Create face dataset using your mobile phone for your face as well as faces of 9 other friends.Create multiple variants (at least 5) of each faces with different view angles.

7. Implement binary SVM to classify MNIST digits 3 and 8 using SMO Algorithm. Use different kernel functions(RBF, Polynomial, Linear) and generate ROC curve. Strictly divide(60:20:20) the data into train, validation and test splits. Perform all hyper parameter tuning/feature selection on validation data and report accuracy on test split.

8. Fuzzy addition and designing fuzzy logic systems on Matlab.