A Course in Machine Learning

A Course in #MachineLearning

  • You may obtain the written materials by purchasing a ($55) print copy , by downloading the entire book , or by downloading individual chapters below.
  • If you find errors in the book, please fill out a bug report .
  • If you would like to be informed when new versions of CIML materials are released, please join the CIML mailing list .
  • Any area in which you need to make sense of data is a potential consumer of machine learning.
  • You can get the source code for the book, labs and other teaching materials on GitHub .

Read the full article, click here.


@kdnuggets: “A Course in #MachineLearning”



A Course in Machine Learning

Concrete AI safety problems

Concrete #AI safety problems

  • Advancing AI requires making AI systems smarter, but it also requires preventing accidents – that is, ensuring that AI systems do what people actually want them to do.
  • We think that broad AI safety collaborations will enable everyone to build better machine learning systems.
  • We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety .
  • Many of the problems are not new, but the paper explores them in the context of cutting-edge systems.
  • The paper explores many research problems around ensuring that modern machine learning systems operate as intended.

Read the full article, click here.


@RickKing16: “Concrete #AI safety problems”


We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended. (The problems are very practical, and we’ve already seen some being integrated into OpenAI Gym.)


Concrete AI safety problems