This repository contains all the assignments I completed during my master's studies in machine learning. It includes usages and comparisons of various ML algorithms such as - linear regression, logistic regression, decision tree, random forest, k-means, support vector machine, naive bayes, neural network, gradient boosting, k-nearest neighbors, PCA, clustering. Used sklearn's Breast Cancer dataset for most of the assignments.

hw1_programming_solution.ipynb: The notebook references decision tree and random forest algorithms, but they are used for comparison or discussion.

hw2_programming_solution.ipynb: Compares logistic regression, gradient descent with square loss, stochastic gradient descent with square loss. It also compares AdaBoost's accuracy based on its base learner's depth (decision tree). The results show how the depth of the decision tree influences the boosting algorithm's performance on the breast cancer dataset.

hw3_programming_solution.ipynb: Explores Principal Component Analysis (PCA) for dimensionality reduction. It compares PCA using SVD and PCA using sklearn PCA. Used dataset of Olivetti faces from https://ndownloader.figshare.com/files/5976027 and Wisconsin breast cancer dataset.

hw5_programming_solution.ipynb: Focuses on clustering techniques, including K-Means and PCA, for unsupervised learning tasks.