Python implementation for the assignments of the course BITS F312 ( Neural Network and Fuzzy Logic )

• Linear Regression using batch and stochastic gradient descent

• Ridge regression

• Vectorized linear regression

• Least angle regression

• K-means clustering

• Logistic regression

• Multiclass logistic regression using “One VS All” and “One VS One” multiclass coding techniques

• K-Fold cross-validation

• Likelihood ratio test (LRT)

• Maximum a posteriori (MAP) decision rule

• Maximum likelihood (ML) decision rule

• Multilayer perceptron

• Radial basis function neural network (RBFNN)

• Stacked autoencoder

• Extreme learning machine (ELM) classifier

• Deep layer stacked autoencoder based extreme learning machine

• Convolutional neural network

• Convolutional autoencoder

• Neuro-fuzzy inference system (NFIS) classifier