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Presented at the Institute for Research in Quantitative Finance (Q-Group)
Fall 2017 Seminar, Vancouver BC, 10/16/2017
This is a brief Machine Learning overview for experienced Financial Quants who already know the math. The presentation is intended to fill in the gaps regarding what ML is and is not and differentiate what a quant needs to know from the cacophony of hype in the current environment. It is a tool to help the reader decide whether to invest the time necessary to master the basic methods of ML and use them in a way that tangibly benefits the investment process. If you just want to put Deep Learning on your resume, or toss Big Data into a blender and start backtesting, or use canned libraries without quantifiable confidence bounds and rational justification for your results, while avoiding troublesome mathematics and laborious thought, this probably not a good resource. At the end of the presentation, there is a list of books to read which have exercises you must perform in order to do the actual learning. Expect to spend three to twelve months depending on how far you want to take it.