lmlcr

Lightweight Machine Learning Classics with R (Book Draft)

https://github.com/gagolews/lmlcr

Science Score: 67.0%

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Keywords

classification clustering data-science machine-learning machine-learning-algorithms mathematical-modelling optimisation-algorithms r regression
Last synced: 4 months ago · JSON representation ·

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Lightweight Machine Learning Classics with R (Book Draft)

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Topics
classification clustering data-science machine-learning machine-learning-algorithms mathematical-modelling optimisation-algorithms r regression
Created almost 6 years ago · Last pushed over 3 years ago
Metadata Files
Readme License Code of conduct Citation

README.md

Lightweight Machine Learning Classics with R

(Open Access Lecture Notes)

DOI

Explore some of the most fundamental algorithms which have stood the test of time and provide the basis for innovative solutions in data-driven AI. Learn how to use the R language for implementing various stages of data processing and modelling activities. Appreciate mathematics as the universal language for formalising data-intense problems and communicating their solutions. These lecture notes are for you if you're yet to be fluent with university-level linear algebra, calculus and probability theory or you've forgotten all the maths you've ever learned, and are seeking a gentle, albeit thorough, introduction to the topic.

About this Repository

This repository hosts the HTML version of the lecture notes. You can read it at:

  • https://lmlcr.gagolewski.com/ (a browser-friendly version)
  • https://lmlcr.gagolewski.com/lmlcr.pdf (PDF)

About the Author

Marek Gagolewski is currently a Senior Lecturer in Applied AI at Deakin University in Melbourne, VIC, Australia and an Associate Professor in Data Science (on long-term leave) at the Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland and Systems Research Institute of the Polish Academy of Sciences.

His research interests include machine learning, data aggregation and clustering, computational statistics, mathematical modelling (science of science, sport, economics, etc.), and free (libre) data analysis software (stringi, genieclust, among others).


Copyright (C) 2020-2022, Marek Gagolewski.

This material is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

Owner

  • Name: Marek Gagolewski
  • Login: gagolews
  • Kind: user
  • Location: Melbourne, VIC, Australia
  • Company: Deakin University

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Citation (CITATION.cff)

cff-version: 1.2.0
message: "Please cite this book as below."
authors:
  - family-names: Gagolewski
    given-names: Marek
    orcid: "https://orcid.org/0000-0003-0637-6028"
repository-code: "https://github.com/gagolews/lmlcr"
title: "Lightweight Machine Learning Classics with R"
doi: 10.5281/zenodo.3679976
preferred-citation:
    type: book
    year: 2022
    url: "https://lmlcr.gagolewski.com/"
    title: "Lightweight Machine Learning Classics with R"
    doi: 10.5281/zenodo.3679976
    authors:
    -   family-names: Gagolewski
        given-names: Marek
        orcid: "https://orcid.org/0000-0003-0637-6028"

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