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