Machine Learning in Finance, New York
Sessions of this course will cover opportunities and limitations, portfolio construction, ML for trading, risk management and NLP in credit markets.
This two day training course will build a strong foundation in machine learning by examining its theory, technical approaches, solutions and how to make better decisions and apply machine learning methods in your organization.
Led by top practitioners from leading firms in the financial industry the course will provide delegates with an in-depth understanding of the best capabilities of machine learning techniques and tools in portfolio construction, trading, risk management and beyond.
Latest approaches to machine learning applications in finance from a quantitative viewpoint
Machine learning and robust portfolio construction
The impact of data quality on data analysis
How to apply natural language processing
An introduction to deep learning neural networks
Machine learning for long-term investing
Introduction to ML, early financial applications and deep learning neural networks
ML for long-term investing
Big data's dirty secret
A deeper dive on natural language processing
Deep learning for sequences and timeseries
Speeding up option pricing with VDR
ML and robust portfolio construction
We offer flexible pricing options for this course:
Early bird rates - save up to $400
Group booking rate - save up to $2000
Add Python for Financial Markets course - save over $1000
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Founder and principal
Terry Benzschawel recently started his own firm after 30 years as a quant on Wall Street. The firm specializes in financial education, advanced model development, and systematic trading. Before that, Terry was a Managing Director in Citigroup's Institutional Clients Business, heading the Quantitative Credit Trading group.
Terry received a Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). Terry has done post-doctoral fellowships in Optometry, Ophthalmology, and engineering prior to embarking on a career in finance. Terry began his financial career in 1988 at Chase Manhattan Bank, building genetic algorithms to predict corporate bankruptcy. In 1990, he moved to Citibank and trained a neural network to detect fraud on credit card transactions. In 1992 he was hired by Salomon's Fixed Income Arbitrage Group to build models for proprietary fixed income trading. In 1998, he moved to Citi’s Fixed Income Strategy department as a credit strategist with a focus on client-oriented solutions across all credit markets where he worked in related roles since then. Terry is a frequent speaker at industry conferences and events and has lectured on credit modelling at major universities and government institutions. In addition, he has published over a dozen articles in refereed journals and has authored two books: CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS and CREDIT MODELING: ADVANCED TOPICS.
Founder, GARRETT ASSET MANAGEMENT and Lecturer
Dr. Noma teaches machine learning at Columbia University and quantitative risk management in the Masters in Mathematical Finance program at Rutgers University. Earlier in his career, Dr. Noma was a professor in the Psychology Department of Rutgers University, publishing in the areas of statistics, psychometrics, applied decision making and group dynamics.
Dr. Noma is also the founder of Garrett Asset Management, an investment firm that uses behavior models to trade in futures, ETFs, and currencies. Prior to founding Garrett Asset Management, Dr. Noma was a portfolio manager running a fund of hedge funds and was the Chief Risk Officer at Asset Alliance, a seeder of hedge funds.
Dr. Noma currently advises fintech companies in the application of machine learning, natural language processing and blockchain technologies.
Dr. Noma graduated from Dartmouth College with a B.A. in Mathematics. He received an M.A. in Mathematics and a Ph.D. in Mathematical Psychology from The University of Michigan.
NYU Tandon School of Engineering
Ken created the Risk Management department at Och Ziff and served as Chief Risk Officer for over 13 years. He led the Firm through a five-fold increase in AUM and headcount, the transition from private to public company, and managed major and minor financial/business crises and the introduction of new strategies and products.
He pioneered the use of quantitative techniques for portfolio construction and analysis at a fundamentally oriented firm, anticipating the “quantamental” revolution. Most recent activities have focused on how Artificial Intelligence may adapted to Finance.
U.S. Head of Multi-Assets and Quantitative Solutions
BNP Paribas Asset Management
Patrick Dugnolle, PhD, U.S. Head of Multi-Asset and Quantitative Solutions
Patrick is the U.S. Head of Multi-Asset and Quantitative Solutions at BNP Paribas Asset Management. In this role he is responsible for developing the strategy and products for our Multi-Asset and Quantitative Solutions business in the U.S. He is based in the firm’s New York office.
He joined BNP Paribas more than 16 years ago and his contribution to quantitative factor investing dates back to 2004 when he joined the Risk-Managed-Funds Research Team of CooperNeff Advisors in King of Prussia, PA. Patrick moved back to Paris in 2006 in order to manage a $100 million long-short portfolio of U.S. equities, and later he became Head of Financial Engineering for Fixed Income for BNP Paribas Asset Management. Since 2013, Patrick has largely contributed to the reengineering of the Multi-Factor Quantitative Equity Investment process, which currently has over $2 billion under management. Prior to moving to New York City in September 2018, he was managing more than $1.5 billion of assets invested in U.S., European and global equities.
Patrick holds a PhD in Computer Science applied to Theoretical Biology from the University Joseph Fourier of Grenoble and an Engineer Degree in Electronics from ICPI Lyon.
Alexander Fleiss serves as CEO of RebellionResearch.com an online financial advisory & hedge fund that invests across all asset classes and utilizes a proprietary Machine Learning that monitors data from 53 countries on a daily basis. Mr. Fleiss has spoken about Artificial Intelligence investing in the Wall Street Journal, Fox News, BusinessWeek, Bloomberg News, MIT Technology Review, Wired, Mathematical Association of America, Financial Times, CNBC, Geo Magazine, Institutional Investor and the Wall Street Journal Reporter Scott Patterson’s book Dark Pools. In addition, Mr. Fleiss has lectured on Artificial Intelligence & Machine Learning at Princeton University, Amherst College, Yale School of Management, Booth School of Business at the University of Chicago, Tufts University, Cornell University, The Wharton School of Business at The University of Pennsylvania and Columbia Business School.
Prior to co-founding RebellionResearch.com in 2007, Mr. Fleiss served as a Principal at KMF Partners LP, a long-short US equity fund. Mr. Fleiss began his investment career as an analyst for Sloate, Weisman, Murray & Co which was acquired by Neuberger Berman. Mr. Fleiss developed investment algorithms with the firm’s CEO, Laura Sloate who is now a partner at Neuberger Berman and is one of the investors featured in Peter Tanous’ book Investment Gurus. Mr. Fleiss received a BA Degree from Amherst College.
Head of the quantitative risk analytics group
Dr. Harvey J. Stein is Head of the Quantitative Risk Analytics Group at Bloomberg, responsible for all quantitative aspects of Bloomberg's risk analysis products. Dr. Stein is well known in the industry, having published and lectured on mortgage backed security valuation, CVA calculations, interest rate and FX modeling, credit exposure calculations, financial regulation, and other subjects. Dr. Stein is also on the board of directors of the IAQF, an adjunct professor at Columbia University, a board member of the Rutgers University Mathematical Finance program and of the NYU Enterprise Learning program, and organizer of the IAQF/Thalesians financial seminar series. He received his BA in mathematics from WPI in 1982 and his PhD in mathematics from UC Berkeley in 1991.
This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event in accordance with the standards of the National Registry of CPE Sponsors.
55 Broad Street
55 Broad Street, 22nd Floor
New York, NY 10004
An important course for anyone wanting to keep up with many aspects of the Ibor transition in North America
At Infopro Digital we are committed to taking a sustainable approach to our end to end event management. We will strive to put sustainability at the forefront of everything we do.
Providing the best experience for our customers whilst maintaining a positive impact on others as well as our environment.
Our main objective in 2020 is to cease onsite printing at our events. To reduce the amount of printed branding materials and reuse as much as we can. We will select seasonal and local produce for catering where possible to reduce our carbon footprint. We pledge a zero tolerance to single use plastic at all of our events.