FMLProjects

Posting my work of a course taken at University of Florida on Fundamentals of Machine Learning:

Homework 1 : MLE and MAP

1. Homework 1 contains the very fundamentals of how a machine learning problem is to be solved.
2. It majorly contains data analysis and preprocessing, generating a correlation matrix and heatmap of the same.
3. Later, there is a pair plot for every field with respect to the count(the result to be predicted).
4. Thorough analysis of which field is useful and which is not
5. Prediction using method is better for the given data MLE or MAP.
6. Comments on Model Performance.

Homework 2: Probability Density Function and K-NN algorithm

1. The process included splitting the given data into training and validation.
2. Calculated parameters like mean and variance of the data for the probability density function(Gaussian in this case) .
3. Predicted the labels and compared with the true value.
4. Implemented K nearest neighbours algorithm from scratch and made the confusion matrix

Homework 4: Manifold Learning

1. Implemented MDS, ISOMAP and LLE from scratch.
2. Visualized the clusters, the half moon dataset and the swiss roll dataset in the smaller dimensional space.
3. Compared which methods work best for which dataset.