{"id":19279521,"url":"https://github.com/nghorbani/machinelearning","last_synced_at":"2025-10-08T01:48:26.170Z","repository":{"id":155841440,"uuid":"78407914","full_name":"nghorbani/MachineLearning","owner":"nghorbani","description":"Some of experiences in Machine Learning field","archived":false,"fork":false,"pushed_at":"2017-07-18T14:04:56.000Z","size":12834,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-06-29T12:40:47.599Z","etag":null,"topics":["bayesian","bayesian-methods","bayesian-optimization","classification","gaussian-processes","hyperparameters","matlab","optimization","regression","simple"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nghorbani.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2017-01-09T08:15:06.000Z","updated_at":"2017-08-30T20:16:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"ece2e786-69df-4a75-accc-69692e161961","html_url":"https://github.com/nghorbani/MachineLearning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nghorbani/MachineLearning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nghorbani%2FMachineLearning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nghorbani%2FMachineLearning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nghorbani%2FMachineLearning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nghorbani%2FMachineLearning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nghorbani","download_url":"https://codeload.github.com/nghorbani/MachineLearning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nghorbani%2FMachineLearning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278877088,"owners_count":26061380,"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","status":"online","status_checked_at":"2025-10-07T02:00:06.786Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["bayesian","bayesian-methods","bayesian-optimization","classification","gaussian-processes","hyperparameters","matlab","optimization","regression","simple"],"created_at":"2024-11-09T21:15:29.009Z","updated_at":"2025-10-08T01:48:26.139Z","avatar_url":"https://github.com/nghorbani.png","language":"Jupyter Notebook","readme":"# Some Machine Learning Experiences\n\n## Bayesian Optimization with Gaussian Processes\n[**BO in Python**](BayesianOptimization/BayesianOptimization.ipynb)\n\nYou will see an example of optimizing a simple Neural Network's Hyperparameters with Bayesian Optimization\n\nUsing Expected Improvement acquisition function:\n![alt tag](images/OB1DSample.gif)\n\n## Gaussian Process Regression and Classification\n\n[**GPR in Python**](GaussianProcesses/GRP.ipynb)\n[or in matlab](GaussianProcesses/GPR.m)\n\n[**GPC in Python**](GaussianProcesses/GPC.ipynb)\n[or in matlab](GaussianProcesses/GPC.m)\n\n### Regression with Gaussian Processes\nWe have noisy sensor readings (indicated by errorbars).\nfirst we will do a point prediction:\n\n![alt tag](images/GRP-single.png)\n\nNext we will predict 100 points\n\n![alt tag](images/GPR-multi.png)\n\nAnd we finally use MAP estimate of the hyperparameters:\n\n![alt tag](images/GPR-optim.png)\n\n### Classification with Gaussian Processes\nWe have generated training points and labels and then tried to compute labels for test points.\nEach + is one trainig point and * is a test point:\n\n![alt tag](images/GPC.png)\n\nIf the color of '*' and 'o' coincide then that point is correctly classified. The points without any circle around them are training points ('+').\n\n## Linear Regression (LR) and Classification (LC)\n\n### LR in high-dimensional feature space\n\n[matlab code](LinearRegression/main.m)\n\n![alt tag](images/LR_with_basis.png)\n\nHere have projected input to a hd feature space with the help of basis functions and followed the rest of the standard linear model for regression. We see effect of different choices for basis function hyperparameters.\n\n### LC with Linear and Quadratic Decision Boundaries\n\n[matlab code](LinearClassification/linearclassification.m)\n\n![alt tag](images/LC_LDA.png)\n\n![alt tag](images/LC_QDA.png)\n\n\n\n## Sampling Techinques\n\n### Sampling from a cauchy distribution\n\n[matlab code](Sampling/cauchysampling.m)\n\n![alt tag](images/cauchysampling.png)\n\n\n## Principle Component Analysis\n\n### Basics of PCA\n\n[matlab code](DimenstionalityReduction/pca_basics.m)\n\n![alt tag](images/pca_basics.png)\n\nin the folder you can also find code for PCA with isotropic noise.\n\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnghorbani%2Fmachinelearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnghorbani%2Fmachinelearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnghorbani%2Fmachinelearning/lists"}