This repository is the collection of ideas from being asked to explain machine learning. Initially, this was for an informal talk to the University of Oregon Society of Physics Students.

• There are two versions of the intro talk (PowerPoint and Keynote).
• The "TransferLearningClass" slides are about the idea of training a neural network for one task, and then re-purposing it for a similar task.
• The "GradDescentGood.mp4" is a video clip showing how gradient descent works, and it was made using the Mathematica notebook called "GradientDescent.nb"
• The Slides directory contains two lectures on machine learning for high energy physics which were given at the CTEQ2019 summer school.

The tutorials from the CTEQ2019 summer school can be found in the Tutorials directory. Much of this was copied from the homeworks of the second class listed below.

• 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.

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

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