[College Project] - BITS F312
Implementation of different machine learning algorithms with the help of matlab, python3 and jupyter notebook. You can also look at implementation of cross validation partition, both using k-fold and holdout splits using iris dataset. Autoencoders are also implemented using the same.
Did respective college (3)assignments.
| Problems | Assignment-1 |
|---|---|
| 1 | Linear Regression |
| 2 | Stochastic Gradient Descent |
| 3 | Ridge Regression |
| 4 | Vectorized Linear Regression |
| 5 | Least Angle Regression |
| 6 | unsupervised learning |
| 7 | Logistic Regression for binary class |
| 8 | Logistic Regression for multi class |
| 9 | Logistic Regression for multi class |
| 10 | Likelihood ratio test |
| 11 | Maximum A Posteriori |
| 12 | Maximum Likelihood |
| Problems | Assignment-2 |
|---|---|
| 1 | Multi layer perceptron based Neural Network(2 hidden layers) |
| 2 | Radial basis function NN |
| 3 | Stacked Autoencoder |
| 4 | Extreme learning machine |
| 5 | Stacked Autoencoder+ELM |
| Problems | Assignment-3 |
|---|---|
| 1 | Convolutional Neural Network |
| 2 | Conv2D Transpose |
| 3 | ANFIS |
This was challenging since it was my first time using matlab,and doing data analysis using it.
- Python3.6
- Matlab
- keras
- sklearn
- xlsread, ones, size, mean, std, randperm, length, plot, xlabel, ylabel, clear, plot3, contour, scatter, sqrt, sum, find, display, confusionmat, trace, min, cvpartition, norm, kmeans, max, trainAutoencoder, encode, stack, train, stackednet, pinv, tanh, randn, normpdf