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Intro To Machine Learning

This contains information from a talk given to the University or Oregon Society of Physics Students (SPS). The Mathematica notebook shows how Gradient Descent works in the context of a 2-dimensional minimization problem. The associated video shows the minimization happening. There are also two versions of the talk (keynote and power point).


Online classes

  • This class is a little older, and does the programming in Octave instead of python, but is a great class. This goes over many techniques beyond neural networks.

  • An updated version does things with python and uses some of the standard tools. It focuses more on deep learning.

Python Packages

  • Scikit-Learn makes machine learning very easy.
  • Keras is the package I use for neural networks.

Datasets and challenges

While there is not necessarily much open data in high energy physics, there is a lot of other data to learn from.

  • Kaggle hosts many datasets and some challenges. Users upload their scripts, which is a great resource for learning the techniques. In addition, one of the hosted challenges was to use ATLAS data to find the Higgs!
  • Data Driven is another site which offers challenges and prizes.
  • HackerRank is not necessarily for machine learning, but a great place to practice programming. I highly recommend it. It offers coding challenges for prizes.
  • CERN open data I don't have any experience with either of these open data resources, other than knowing they exist.
  • CMS open data
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