Chapter 16 Machine learning

Machine learning is a growing field of data analysis where the building of models is iterated automatically. This is particularly important in areas where new data is being collected on an on-going basis (examples often used are Netflix recommendations and amazon`s “Recommended for you”).

16.1 Theory and methods

David Robinson, 2018-01-09, “What’s the difference between data science, machine learning, and artificial intelligence?”

Lev Reyzin, 2019-01-07, “Unprovability comes to machine learning”, Nature

Nicholas Harvey, CPSC 531H: Machine Learning Theory, UBC Computer Science Department.

  • “This is a graduate course on some theoretical aspects of machine learning. The emphasis is on foundations and on results with rigorous proofs. The viewpoint is much more computational than statistical.”

Audrey Lorberfeld, 2019-03-01, Machine Learning Algorithms In Layman’s Terms, Part 1

16.1.1 Deep learning

Sigrid Keydana, 2019-03-14, Math, code, concepts: A third road to deep learning {at the TensorFlow for R blog}

16.2 R

Laurent Gatto, 2019-04-24, An Introduction to Machine Learning with R

J.J. Allaire, Machine Learning with R and TensorFlow, keynote at rstudio::conf 2018 (youtube video)

David Smith, 2018-02-21, Machine Learning in R with TensorFlow

Coursera: Practical Machine Learning

Karlijn Willems, [Machine Learning in R for beginners] {link to DataCamp course removed}

Jason Brownlee, 2016-02-23, Your First Machine Learning Project in R Step-By-Step (tutorial and template for future projects)

Brad Boehmke & Brandon Greenwell, Hands-on Machine Learning with R

Khushbu Shah, 2016-06-06, “What are the Best Machine Learning Packages in R?”

16.2.1 {caret}

Jason Brownlee (2016-02-03) Your First Machine Learning Project in R Step-By-Step – at Machine Learning Mastery

16.2.2 {h2o}


CRAN page: h2o: R Interface for H2O


Erin LeDell, 2017, Automatic Machine Learning in R – presentation at UseR! Brussels

16.2.3 {mlr}

Machine Learning in R