This repository serves as a showcase of the machine learning approaches studied and implemented as part of the Machine Learning for Data Science course at the Data Science Masters' course at DF@FRI UL. All work is my own.
It is advised to look at individual .pdf reports in each folder for better understanding of the provided code.
Own implementation of classification trees, bagging, and random forests on the housing dataset, with use of Gini Index for best split optimization. Reported misclassification rates for predefined hyperparameters.
Own implementation of multinomial and ordinal logistic regression with MLE. Proposal of dataset on which the ordinal approach works notably better. Interpretation of model coefficients on a practical application, with quantification of uncertainty.
NOTE: The solution in this report contains faulty selection of columns (only first two) on the Housing Dataset. The study of the application of kernels: notably ridge regression. Application of an own implementation to both a toy and practical dataset.
Own implementation of SVR, on toy and practical dataset., with two kernels. Comparison to results from HW3 and a comment on differences and similarities.
Own implementation of holdout estimation and cross validation, with several demonstration on selection methods' effect on model risk estimation and split/test data variability.
Own implementation of a multi layer, fully connected ANN, with numerical verification of the gradient and application to a classification and regression dataset. Qualitative reasoning for performance differences on different hidden layer configurations is also provided.