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J.P. Morgan's Comprehensive Guide on Machine Learning

At 280 pages, the report is too long to cover in detail, but we’ve pulled out the most salient points for you below.

Main Points

  • Banks will need to hire excellent data scientists who also understand how markets work
  • Machines are best equipped to make trading decisions in the short and medium term
  • An army of people will be needed to acquire, clean, and assess the data 
  • There are different kinds of machine learning. And they are used for different purposes
  • Supervised learning will be used to make trend-based predictions using sample data
  • Unsupervised learning will be used to identify relationships between a large number of variables
  • Deep learning systems will undertake tasks that are hard for people to define but easy to perform
  • Reinforcement learning will be used to choose a successive course of actions to maximize the final reward
  • You won’t need to be a machine learning expert, you will need to be an excellent quant and an excellent programmer
  • These are the coding languages and data analysis packages you’ll need to know
  • And these are some examples of popular machine learning codes using Python
  • Support functions are going to need to understand big data too

Read more here

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