MLLearn

This repo contains course projects from my graduate Machine Learning course

module_1

• Includes low-level implementation of Multi-Nomial Naive Bayes, Discrete Naive Bayes and Logsitic Regression

module_2

• Low-level implementation of Collaborative Filtering and running on the Netflix Dataset
• KNN, Neural Networks and SVM using sklearn

module_3

• Low-level implementation of image compression using K-means clustering
• Comparing Decision Trees, Gradient Boosting and Random Forest using sklearn

module_4

• Chow Liu Tree for Tree Bayesian Network
• Mixture of Tree Bayesian Network(involves a latent variable)
• Mixtures of Tree Bayesian networks using Random Forests

Each module contains readme.md file with instructions to run the scripts.