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.


Machine Learning in Finance:

A Quantitative Approach 

December 4–5, 2019

New York

Course guide   Apply now

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 the leading firms in the financial industry the course will provide delegates with best capabilities of machine learning techniques and tools in portfolio construction, trading, risk management and beyond. 

What will you learn?
  • In-depth understanding about how machine learning is changing the financial industry

  • The theory behind machine learning, earlier and latest applications of machine learning and how main methods can be applied in your firm

  • Latest approaches to ML applications in finance from a quantitative viewpoint

  • ML in risk management and trading 

  • Regulatory requirements of AI/ML models

  • Challenges and opportunities of ML tools on portfolio construction and optimization 

  • Understanding the relationship between deep learning and big data

  • ML and AI capabilities- how they can help you solve problems more effectively and drive your business forward

View course guide

Who should attend?

Relevant departments may include but are not limited to: 

  • Risk management 

  • Machine learning

  • Portfolio management

  • Wealth management 

  • Asset allocation

  • Data science

  • Financial engineering

  • Quantitative analytics

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Course highlights
  • Introduction to machine learning and early financial applications

  • Recent applications of machine learning in finance

  • Alternative data for investors 

  • A deeper dive on natural language processing  

  • Applying NLP to earning calls transcripts in equity and credit markets

  • Machine learning in trading and portfolio optimization    

  • The impact of machine learning from a regulatory perspective

  • Machine Learning for fraud and AML 

View course agenda

Petter Kolm

Professor & director of the mathematics in finance, Courant Institute

New York University

Petter Kolm is the Director of the Mathematics in Finance Masters Program and Clinical Associate Professor at the Courant Institute of Mathematical Sciences, New York University and the Principal of the Heimdall Group, LLC. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group's hedge fund. Petter coauthored the books Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006), Trends in Quantitative Finance (CFA Research Institute, 2006), Robust Portfolio Management and Optimization (Wiley, 2007), and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). He holds a Ph.D. in mathematics from Yale, an M.Phil. in applied mathematics from Royal Institute of Technology, and an M.S. in mathematics from ETH Zurich.

Petter is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Investment Strategies (JOIS), Journal of Portfolio Management (JPM), and the board of directors of the International Association for Quantitative Finance (IAQF). As a consultant and expert witness, he has provided his services in areas such as algorithmic and quantitative trading strategies, econometrics, forecasting models, portfolio construction methodologies incorporating transaction costs, and risk management procedures.


Terry Benzschawel

Founder and principal

Benzschawel Scientific

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.

Jesús Calderón

Managing director


Alexander Fleiss


Alexander Fleiss serves as CEO of 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 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.

CPE Accreditation

CPE Member

CPE Accreditation

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

Financial District

New York, NY 10004