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Deeper Than Machine Learning Is Deep Learning

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Mark Melin
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With machine learning receiving a significant amount of attention in finance, the UBS Global Quantitative research team wanted to recognize how deep learning, a related but more discerning aspect of artificial intelligence recognition, might benefit investors. To tackle the issue UBS called Matthew Dixon, Professor of Finance & Statistics at the Illinois Institute of Technology, to explain how very high dimensional input and hierarchical, data compression, multi-layer networks might benefit a stock portfolio. Dixon has experience working in the banking and high frequency trading in addition to holding a Ph.D. from the Imperial College London, all which points to the cutting edge of the cutting edge in computational finance.

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Mark Melin is an alternative investment practitioner whose specialty is recognizing the impact of beta market environment on a technical trading strategy. A portfolio and industry consultant, wrote or edited three books including High Performance Managed Futures (Wiley 2010) and The Chicago Board of Trade’s Handbook of Futures and Options (McGraw-Hill 2008) and taught a course at Northwestern University's executive education program.