This repository will consist homework of assignments and projects from the machine learning class at the University of North Carolina at Charlotte.
Linear Regression and Gradient Descent
Regularization: Penalizing larger sample values that may result in overfitting our training model.
Logistic Regression | PCA | Bayes Naive Classification | K-Principle Components
Support Vector Machine (SVM) | Support Vector Regression (SVR)
Pytorch | Optimizers: Adam and Schotastic Gradient Descent (SGD)
Fully Connected Neural Networks (FCNN)
Convolutional Neural Networks (CNN)| Regularization | Residual Networks(ResNet)
Predicting The Future Returns of the S&P 500
machine learning models: SVM Fully Connected Neural Network Convolutional Neural Network