{"id":25733707,"url":"https://github.com/saniyaabushakimova/machine-learning-algorithms-from-scratch","last_synced_at":"2026-04-18T03:31:42.326Z","repository":{"id":279429414,"uuid":"938775576","full_name":"SaniyaAbushakimova/Machine-Learning-Algorithms-From-Scratch","owner":"SaniyaAbushakimova","description":null,"archived":false,"fork":false,"pushed_at":"2025-02-25T14:40:04.000Z","size":5169,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-25T14:45:00.307Z","etag":null,"topics":["gmm-em","hmm-viterbi-algorithm","knn","lasso-regression","pegasos-learning-algorithm","python","sgd","splines","svm"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SaniyaAbushakimova.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-25T13:38:12.000Z","updated_at":"2025-02-25T14:44:34.000Z","dependencies_parsed_at":"2025-02-25T14:55:08.039Z","dependency_job_id":null,"html_url":"https://github.com/SaniyaAbushakimova/Machine-Learning-Algorithms-From-Scratch","commit_stats":null,"previous_names":["saniyaabushakimova/machine-learning-algorithms-from-scratch"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SaniyaAbushakimova%2FMachine-Learning-Algorithms-From-Scratch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SaniyaAbushakimova%2FMachine-Learning-Algorithms-From-Scratch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SaniyaAbushakimova%2FMachine-Learning-Algorithms-From-Scratch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SaniyaAbushakimova%2FMachine-Learning-Algorithms-From-Scratch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SaniyaAbushakimova","download_url":"https://codeload.github.com/SaniyaAbushakimova/Machine-Learning-Algorithms-From-Scratch/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240790290,"owners_count":19858010,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["gmm-em","hmm-viterbi-algorithm","knn","lasso-regression","pegasos-learning-algorithm","python","sgd","splines","svm"],"created_at":"2025-02-26T04:22:29.605Z","updated_at":"2026-04-18T03:31:42.320Z","avatar_url":"https://github.com/SaniyaAbushakimova.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# About\n\nThis repository contains implementations of various **Machine Learning and Statistical Learning algorithms from scratch**, developed as part of the **Practical Statistical Learning** and **Deep Learning for Computer Vision** courses. Each project focuses on building models without relying on high-level machine learning libraries, providing deeper insights into their mathematical foundations and optimizations.\n\nEach folder contains:\n* `ipynb` files with implementation (Python).\n* Corresponding datasets.\n* Instructions on implementation details.\n\n## Implemented Algorithms and Projects\n\n### 1. GMM-and-HMM-with-Expectation-Maximization\n*Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM) using the EM Algorithm*\n\nProject completed on October 20, 2024.\n\n* Implemented Expectation-Maximization (EM) from scratch to fit GMMs for clustering and density estimation.\n* Developed Baum-Welch (EM for HMMs) and Viterbi Algorithm to train and decode Hidden Markov Models.\n* Applied the models to sequence modeling and probabilistic clustering.\n\n### 2. KNN-and-Bayes-Classification\n*Comparing k-Nearest Neighbors (kNN) and Bayes Rule for Classification*\n\nProject completed on September 6, 2024.\n\n* Implemented custom kNN classifier with cross-validation for hyperparameter selection.\n* Developed Bayes Classifier from scratch, leveraging probability distributions for decision-making.\n* Conducted a simulation study to compare kNN and Bayes decision rules in different distributions.\n\n### 3. LOESS-RidgelessRegression-NCS\n*Nonparametric Regression and Overfitting in High-Dimensional Models*\n\nProject completed on September 30, 2024.\n\n* Implemented LOESS (Locally Weighted Scatterplot Smoothing) for nonlinear regression.\n* Explored Ridgeless Regression to analyze overfitting and the Double Descent phenomenon.\n* Used Natural Cubic Splines (NCS) for time series smoothing and feature extraction.\n\n### 4. Lasso-with-Coordinate-Descent\n*Sparse Regression with L1 Regularization*\n\nProject completed on September 18, 2024.\n\n* Implemented Lasso Regression from scratch using the Coordinate Descent algorithm.\n* Compared Lasso with Ridge Regression and Principal Component Regression (PCR).\n* Analyzed model sparsity and feature selection using simulated datasets.\n\n### 5. SVM-with-Pegasos-Algorithm\n*Support Vector Machines (SVM) using a Specialized SGD Method*\n\nProject completed on November 12, 2024.\n\n* Developed Support Vector Machines (SVM) from scratch, solving the primal form directly.\n* Implemented Pegasos Algorithm (Primal Estimated sub-GrAdient SOlver for SVM), a variation of Stochastic Gradient Descent (SGD) optimized for large-scale datasets.\n* Applied the model to binary classification tasks on MNIST subsets and evaluated generalization performance.\n\n### 6. Linear Classifiers: Perceptron, SVM, Softmax, Logistic Regression\n\nProject completed on February 20, 2025.\n\n* Implemented Perceptron, SVM, Softmax, and Logistic Regression classifiers from scratch in Python and applied them to the **Rice** and **Fashion-MNIST** datasets.\n* Focused on understanding core concepts of linear classification, hyperparameter tuning, and performance evaluation using training/validation/test splits.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaniyaabushakimova%2Fmachine-learning-algorithms-from-scratch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaniyaabushakimova%2Fmachine-learning-algorithms-from-scratch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaniyaabushakimova%2Fmachine-learning-algorithms-from-scratch/lists"}