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Thu, 26 Oct 2017 02:20:50 GMTSlideShare feed for Slideshows by User: WalterTackettPhDCFAAn Intro to Machine Learning For Quant Investment: "Eventually Probably Approximately Correct", Presented at Q-Group Fall 2017 Seminar
https://www.slideshare.net/WalterTackettPhDCFA/an-intro-to-machine-learning-for-quant-investment-eventually-probably-approximately-correct-presented-at-qgroup-fall-2017-seminar
tackettqfall17epaci2ml4qdistribfinalr02-171026022050 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.]]>
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.]]>
Thu, 26 Oct 2017 02:20:50 GMThttps://www.slideshare.net/WalterTackettPhDCFA/an-intro-to-machine-learning-for-quant-investment-eventually-probably-approximately-correct-presented-at-qgroup-fall-2017-seminarWalterTackettPhDCFA@slideshare.net(WalterTackettPhDCFA)An Intro to Machine Learning For Quant Investment: "Eventually Probably Approximately Correct", Presented at Q-Group Fall 2017 SeminarWalterTackettPhDCFAPresented 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.<img alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tackettqfall17epaci2ml4qdistribfinalr02-171026022050-thumbnail-2.jpg?cb=1508986609" style="border:1px solid #C3E6D8;float:right;" /><br> 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.

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https://cdn.slidesharecdn.com/profile-photo-WalterTackettPhDCFA-48x48.jpg?cb=1528931535I am the founder and Managing Partner of NTrillion (nE12.com), a firm specializing in computational investment strategies and risk management for both traditional and digital asset classes.
The investment industry is in the early stages of a transformational era that will see algorithms - adaptive, cryptographic, and distributed - change not only the way investments and risks are managed, but the very nature of customer relationships, compliance, and money. You can read more about our mission at https://nE12.com.
I have worked as a Quant and Team Leader in the Investment industry since 2002, with a focus on problem-solving to improve investment and risk management. Prior to that, ...nE12.com