{"id":20457575,"url":"https://github.com/syedt1/machine-learning-notes","last_synced_at":"2026-02-08T11:01:02.093Z","repository":{"id":122865594,"uuid":"465333618","full_name":"SyedT1/Machine-Learning-Notes","owner":"SyedT1","description":null,"archived":false,"fork":false,"pushed_at":"2023-03-31T19:48:46.000Z","size":22,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-25T09:58:30.572Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"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/SyedT1.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":"2022-03-02T14:12:48.000Z","updated_at":"2022-03-02T14:12:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"fce01feb-d69e-4302-a167-1d4f328ff177","html_url":"https://github.com/SyedT1/Machine-Learning-Notes","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SyedT1/Machine-Learning-Notes","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SyedT1%2FMachine-Learning-Notes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SyedT1%2FMachine-Learning-Notes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SyedT1%2FMachine-Learning-Notes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SyedT1%2FMachine-Learning-Notes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SyedT1","download_url":"https://codeload.github.com/SyedT1/Machine-Learning-Notes/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SyedT1%2FMachine-Learning-Notes/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29228534,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-08T09:43:19.170Z","status":"ssl_error","status_checked_at":"2026-02-08T09:42:55.556Z","response_time":57,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":[],"created_at":"2024-11-15T12:07:54.589Z","updated_at":"2026-02-08T11:01:02.046Z","avatar_url":"https://github.com/SyedT1.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Advanced Machine Learning Links\n+ [**Reddit Link**](https://www.reddit.com/r/MachineLearning/comments/fdw0ax/d_advanced_courses_update/)\n# Machine-Learning-Notes\n## Students should have basic understanding of the following concepts as per mentioned by the course in the Intro of [this](https://www.youtube.com/watch?v=EfnJXeKmodw\u0026list=PLcXJymqaE9PPGGtFsTNoDWKl-VNVX5d6b\u0026index=1) playlist.\n+ **Linear Algebra**\n+ **Statistics**\n+ **Random Variables**\n+ **Stochastic Processes**\n+ **Optimization for Static and Dynamic Systems**\n+ **Image Processing**\n\n## Supplements of this course\n+ **Convex and Non-Convex Optimization**\n   + **Convex Optimization - Stephen Boyd([Link to the website](https://web.stanford.edu/~boyd/cvxbook/))**\n+ **Estimation Theory**\n\n## Important Books to study to prepare notes of the lecture series [here](https://www.youtube.com/watch?v=EfnJXeKmodw\u0026list=PLcXJymqaE9PPGGtFsTNoDWKl-VNVX5d6b\u0026index=1) (which are highly recommended as well):\n+ **Pattern Classification - Richard O' Duda**\n+ **Statistical Pattern Recognition - Fukunaga**\n+ **Machine Learning - A Probablistic Perspective by Kevin Murphy**\n+ **Pattern Recognition and Machine Learning - Christopher M. Bishop**\n+ **The Elements of Statistical Learning(Data Mining, Interference and Prediction) - Robert Tibshirani**\n+ **A Probabilistic Theory of Pattern Recognition - Luc Devroye**\n+ **Generative Methods**\n    + **Principal Component Analysis-I.T.Jolliffe**\n    + **Independent Component Analysis-Errkki Oja**\n+ **Generative Methods for Classification**\n    + **Discriminant Analysis and Statistical Pattern Recognition - Geoffrey J McLACHLAN**\n+ **Clustering and Unsupervised Learning**\n    + **Finite Mixture Models - Geoffrey J McLACHLAN**\n    + **The EM Algorithm and Extensions- Geoffrey J McLACHLAN**\n+ **Graphical Models**\n    + **Probabilitistic Graphical Models - Principles and Techniques - DAPHNE KOLLER**\n    + **Probabilitistic Reasoning in Intelligent Systems - Judea Pearl**\n\n+ **Statistical Learning**\n    + **Statistical Learning Theory - Vapnik**\n    + **The Nature of the Statistical Learning Theory - Vapnik**\n    + **Spline Models for observation of data - Grace Wahba**\n    + **Learning from Data - Yaser S Abu Mustafa** and **his Lectures' playlist on Youtube**\n    + **Kernel Methods for Pattern Analysis - John Shawe**\n\n+ **Functional Data Analysis**\n    + **Functional Data Analysis - J.O Ramsey**\n\n+ **Deep Learning**\n    + **Deep Learning - Ian GoodFellow**\n\n+ **Combining Classifiers**\n    + **Combining Pattern Classifiers - Ludmila Kuncheva**\n\n+ **Some Other Books to Read for Understanding the content of the above book required for ML Topics**\n   + **Vector Calculus - Anthony Tromba**\n   + **Matrix Computations - Gene H Golub**\n   + **[Introduction to Applied Linear Algebra - Vector, Matrices and Least Squares - Stephen Boyd](https://web.stanford.edu/~boy/vmls/)**\n   + **Numerical Methods for unconstrained optimization and non linear equations - J.E Dennis Jr**\n   + **Understanding the New Statistics - Geoff Cumming**\n   + **Artificial Intelligence - A Modern Approach - Stuart Russell**\n   + **Introduction to Algorithms - Thomas Cormen**\n+ **General Reads:Related to What we're reading/learning from this course**\n   + **Godel, Escher, Bach - An Eternal Golden Braid - Douglas R Hofstadter**\n   + **The Theory of Games and Economic Behavior - Von Neumann**\n   + **The Book of Why - Judea Pearl**\n   + **The Society of Mind - Marvin Minsky**\n   + **From Bacteria to Bach and Back - The evolution of minds - Daniel C. Dennett**\n   + **Advice for a young investigator - Ramon y Cajal**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsyedt1%2Fmachine-learning-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsyedt1%2Fmachine-learning-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsyedt1%2Fmachine-learning-notes/lists"}