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www.aifinanceinstitute.com
Artificial Intelligence in Finance Institute
NEW YORK CITY / MARCH 5 – JUNE 11 2019
NEW YORK CITY / MARCH 5 – JUNE 11 2019
www.aifinanceinstitute.com
The Artificial Intelligence Finance Institute’s (AIFI) mission is to be the
world’s leading educator in the application of artificial intelligence to
investment management, capital markets and risk. We offer one of the
industry’s most comprehensive and in-depth educational programs,
geared towards investment professionals seeking to understand and
implement cutting edge AI techniques.
Taught by a diverse staff of world leading academics and practitioners,
the AIFI courses teach both the theory and practical implementation
of artificial intelligence and machine learning tools in investment
management. As part of the program, students will learn the
mathematical and statistical theories behind modern quantitative
artificial intelligence modeling. Our goal is to train investment
professionals in how to use the new wave of computer driven tools and
techniques that are rapidly transforming investment management, risk
management and capital markets
1
Mission
Artificial Intelligence in Finance Institute2
Miquel Noguer i Alonso PhD – Co-Founder & Chief Science Officer
Miquel Noguer is a financial markets practitioner with more than 20 years of
experience in asset management, he is currently Head of Development at Global
AI ( Big Data Artificial Intelligence in Finance company ) and Head on Innovation and
Technology at IEF.
He worked for UBS AG (Switzerland) as Executive Director for the last 10 years. He
worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006.
He is professor of Big Data in Finance at ESADE and Adjunct Professor at Columbia
University teaching Asset Allocation, Big Data in Finance and Fintech. He received
an MBA and a Degree in business administration and economics in ESADE in 1993.
In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction
(UNED – Madrid Spain).
Michael Oliver Weinberg CFA – Co-Founder & Chief Executive Officer
Michael has 25 years of experience investing directly at the security level and indirectly
as an asset allocator in traditional and alternative assets. He is the Chief Investment
Officer, and a Senior Managing Director of MOV37 and Protégé Partners. His portfolio
management experience includes Soros Fund Management LLC, Credit Suisse First
Boston, and Financial Risk Management (FRM). Michael is a published author and
keynote speaker at conferences and universities. He received an M.B.A. from Columbia
Business School, where he is now also an Adjunct Professor of Finance and Economics,
and a B.S. in Economics from New York University.
The Faculty
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George Lentzas – Professor
George is a statistics expert with a decade of experience in applying quantitative
models in the real world. He has worked in various capacities at leading financial
institutions, such as Morgan Stanley, BNP Paribas, Citigroup, and Hutchin Hill Capital.
He has also held faculty positions at Columbia University and NYU, where he has
taught courses in machine learning and applied statistics and econometrics.
His professional expertise includes the application of statistics, machine learning, and
AI to finance and economics. He is currently the chief data scientist and manager of
Springfield Capital Management. He holds a PhD, MPhil, and BA from Oxford University
and an MPhil from Cambridge University.
Igor Halperin – Professor
Igor Halperin is Research Professor of Financial Machine Learning at NYU Tandon
School of Engineering. Previously, he was an Executive Director of Quantitative
Research at JPMorgan, and before that he worked as a quantitative researcher at
Bloomberg LP. Igor has published articles in finance and physics journals, is a speaker
at financial conferences and has co-authored the book “Credit Risk Frontiers.”
Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc.
in nuclear physics from St. Petersburg State Technical University. He also advises
fintech and data science start-ups and risk management firms.
Artificial Intelligence in Finance Institute4
Larry Rudolph – Professor
Larry Rudolph is a researcher at the MIT Computer Science and Artificial Intelligence
Laboratory. Larry received his PhD also in Computer Science in 1981 from the Courant
Institute at NYU. He was on the faculty at University of Toronto, Carnegie-Mellon
University, and The Hebrew University, before joining MIT as a principal research
scientist, in 1995.
Way back in 1978, he helped start the Ultracomputer, a high performance parallel
computer architecture, many ideas of which can be found in current multi-core
computer chips. VP (Member of Labs) at Two Sigma Investments.
Josh Joseph – Professor
Josh Joseph is the Chief Intelligence Architect of the Bridge, the application arm of
MIT’s Quest for Intelligence Initiative. Previously, Josh was the Chief Science Officer of
Alpha Features, an alternative data distribution platform, and co-founded a proprietary
trading company based on machine learning driven strategy discovery and fully
autonomous trading. Additionally, he has done a variety of consulting work across
finance, life sciences, and robotics. He has a Ph.D. in Aeronautics and Astronautics from
MIT where his research focused on methods for learning models of complex systems
for decision making.
Mickey Atwal – Professor
Mickey Atwal is an associate professor at Cold Spring Harbor Laboratory where he
undertakes machine learning research and builds tools to analyze vast datasets in
cancer genomics and immunology. He was awarded the Winship Herr Award for
Excellence in Teaching a record three times, developing courses at the interface of
machine learning, molecular biology, and neuroscience. He has trained in theoretical
physics from the University of Cambridge, Cornell University, and Princeton University.
www.aifinanceinstitute.com 7
Petter Kolm – Scientific Advisor & Professor
Petter Kolm is a Clinical Professor and the Director of the Mathematics in Finance
Master’s Program at Courant Institute of Mathematical Sciences, NYU. Previously,
Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset
Management. Petter has coauthored numerous academic articles and four books.
He holds a Ph.D. in Mathematics from Yale, an M.Phil. in Applied Mathematics from the
Royal Institute of Technology, and an M.S. in Mathematics from ETH Zurich. Petter is
also on various Board of Directors, editorial and advisory boards.
Gordon Ritter – Scientific Advisor
Gordon Ritter completed his Ph.D. in mathematical physics at Harvard University in
2007, where he published in top international journals. Prior to that he earned his
Bachelor’s degree from the University of Chicago. Gordon is currently a senior portfolio
manager at GSA. Prior to joining GSA, Gordon was a Vice President of Highbridge
Capital Management and a core member of the firm’s statistical arbitrage group.
Concurrently with his positions in industry, Gordon teaches at three of the nation’s
leading MFE programs, including Baruch College and NYU. He has published articles,
and is a speaker at the top industry conferences.
Artificial Intelligence in Finance Institute6
Dr. Peter CarrDr. Peter Carr is the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering.
He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Prior
to joining the financial industry, Dr. Carr was a finance professor for 8 years at Cornell University, after obtaining his
Ph.D. from UCLA in 1989. He has over 85 publications in academic and industry-oriented journals and serves as an
associate editor for 8 journals related to mathematical finance. He has won multiple prestigious Quant awards.
Armando GonzalezArmando Gonzalez is President & CEO of RavenPack, the leading provider of big data analytics for financial
institutions. Armando is an expert in applied big data and artificial intelligence technologies. His commentary and
research has appeared in leading business publications such as the Wall Street Journal, Financial Times, among
many others. Armando holds degrees in Economics and International Business Administration from the American
University in Paris and is a recognized speaker at academic and business conferences across the globe.
Hein Hundal, Ph.D.Hein Hundal, Ph.D. Is the chief scientist at Random Order, Inc. He was a quantitative analyst for D.E. Shaw. Hein
was a Principal Engineer for Raytheon and an Associate Research Engineer at the Pennsylvania State University
(PSU), where he taught mathematics and earned an Honors B.S. degree. He served five years in the U.S. Navy as a
Nuclear Engineer Officer and a Meteorology Division Officer. Hein has a Master’s Degree in Computer Science and
a Ph.D. in Mathematics from Penn State. Dr. Hundal has more than 15 peer-reviewed publications in mathematics
journals and holds two U.S. patents.
Emily L. SprattEmily L. Spratt is an art historian, art technologist, and strategic advisor who has published extensively on the
subjects of visual culture, aesthetic theory, vision technology, and machine learning in the arts. Emily has a B.A.
from Cornell, an M.A. from UCLA, and an M.A. from Princeton. Her doctorate at Princeton is on Byzantine and
Renaissance art. Emily has taught in the Department of Art History at Rutgers and has been the recipient of
numerous international fellowships and awards. She is a consultant and fellow at The Frick Collection and Art
Reference Library.
Advisory Board
Jeannette M. WingJeannette M. Wing is Avanessians Director of the Data Science Institute and a Professor Columbia University.
From 2013 to 2017, she was at Microsoft Research. She is Consulting Professor of Computer Science at
CarnegieMellon. From 2007-2010 she was the Assistant Director of the Computer and Information Science and
Engineering Directorate at the NSF. She received her S.B., S.M., and Ph.D. degrees in Computer Science, from the
Massachusetts Institute of Technology. She has been chair and/or a member of many academic, government,
and industry advisory boards and received various awards.
www.aifinanceinstitute.com 7
The Curriculum
The course, globally offered online, expounds on the theory and implementation of artificial intelligence in finance.
Students are expected to learn the mathematical and statistical theories behind modern quantitative artificial intelligence
modeling. The course will provide education on the theory and practical application of artificial intelligence in finance
through exposure to world leading practitioners and academics.
Structure
Artificial Intelligence in Investment
Management Certificate
3 months program: March 5 – June 11 2019.
Lectures: New York City and globally offered online.
Tuesday and Thursday. 6.00 – 9.00pm.
75 hours: Lectures + Practice + Speakers.
Evaluation: Exam + Project.
Course Fee: $9,500
Ideal Candidates
⁰ Quantitative Analysts
⁰ Computer Scientists
⁰ Risk Managers
⁰ Traders
⁰ Portfolio Managers
⁰ Investment Managers
⁰ Data scientists
We will give a Python Refresher and Mathematics
Refresher/Primer at the beginning of the course.
Programme
Apply online: www.aifinanceinstitute.com/apply-online
Artificial Intelligence in Finance Institute8
Module Contents Date (all 6–9pm) Professor
1 Artificial Intelligence in Finance Landscape Tues, March 05, 2019 Dr Miquel Noguer i Alonso
2 Alternative data Thurs, March 07, 2019 Dr Petter Kolm
3 Econometrics and financial modeling review Tues, March 12, 2019 Dr Petter Kolm
a. Univariate and Multivariate modeling
b. Continuous and Discrete models
c. Time Series Models
d. Linear Factor Models
e. Portfolio Allocation
f. Exercises
Thurs, March 14, 2019 Dr Petter Kolm
4 Python and coding - Primer Tues, March 19, 2019 Dr Gilberto Batres Estrada
a. Python basics
b. Sci-kit Learn
c. XgBoost
d. Keras and Tensorflow
e. NLTK
f. Exercises
Thurs, March 21, 2019 Dr Gilberto Batres Estrada
5 DataRobot Tues, March 26, 2019 Dr John Boersma
6 Machine Learning Modeling and Metrics Thurs, March 28, 2019 Dr Georges Lentzas
a. Preprocessing
i. Features scaling and selection
ii. Dimensionality Reduction
iii. Sampling
b. Learning
i. Model Selection
ii. Cross-Validation
iii. Performance Metrics
iv. Hyperparameter optimization
c. Evaluation
d. Prediction
e. Exercises and Code
Tues, April 02, 2019 Dr Georges Lentzas
7 Supervised Learning Thurs, April 04, 2019 Dr Georges Lentzas
a. Classification
i. Logistic and Softmax Regression
ii. K-Nearest Neighbors
iii. Classification and Regression Trees
iv. Support Vector Machines
v. Exercises and Code
Tues, April 09, 2019 Dr Georges Lentzas
b. Ensemble models Thurs, April 11, 2019 Dr Miquel Noguer i Alonso
Course Timetable
www.aifinanceinstitute.com 9
i. Bagging: Random Forests
ii. Boosting – Adaboost and XGBoost
iii. Exercises and Code
Tues, April 16, 2019 Dr Miquel Noguer i Alonso
c. Regression Thurs, April 18, 2019 Dr Josh Joseph
i. Linear Regression
ii. Penalized Linear Regression: Lasso and Ridge
iii. Non-Linear Regressions
iv. Deep Regressions
Tues, April 23, 2019 Dr Josh Joseph
8 Unsupervised Learning Thurs, April 25, 2019 Dr Mike Atwal
a. Principal Component Analysis
b. Clustering
c. Exercises and Code
Tues, April 30, 2019 Dr Mike Atwal
9 Deep Learning Thurs, May 02, 2019 Dr Larry Rudolph
a. The mathematics of deep learning
i. Mathematical definition
ii. Optimization
iii. Drop out
b. Feedforward Neural Networks
c. Recurrent Neural Networks
d. Long Short Term Memory Networks
e. Convolutional Neural Networks
f. Generative Adversarial Networks
g. Interpretability
h. Exercises and Code
Tues, May 07, 2019 Dr Larry Rudolph
10 Reinforcement Learning Thurs, May 09, 2019 Dr Igor Halperin
a. Markov decision Processes
b. Deep Reinforcement Learning
c. Inverse Reinforcement Learning
Tues, May 14, 2019 Dr Igor Halperin
11 Artificial Intelligence Thurs, May 16, 2019 Dr Miquel Noguer i Alonso
a. Natural Language Processing
i. Theory
ii. Deep Learning for NLP
iii. Applications
iv. Exercises and Code
Tues, May 21, 2019 Dr Miquel Noguer i Alonso
12 Practical Cases I Thurs, May 23, 2019 All professors
a. AI and Investing
b. AI and Risk and Banking
Tues, May 28, 2019 All professors
13 Final Exam Tues, June 04, 2019 Faculty
14 Artificial Intelligence in Finance Project Tues, June 11, 2019
www.aifinanceinstitute.com
To register, please fax or scan and email the completed booking form to:
E-mail: info@aifinanceinstitute.com
E-mail: info@aifinanceinstitute.com / Tel:+1 646 824-1265
DELEGATE DETAILS
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Regular Course Fee Full Course Fee: $9,500.00
Early Bird Discount 25% Discount until Friday February 8 2019
Discount code
VOLUME DISCOUNT: If 2 or more people from your institution wish to take the course please contact us.
FLEXIBLE PAYMENT OPTIONS:
Option 1: • Pay in full on Registration
Option 2: • Pay 50% on registration and 50% by April 11 2019
Registration Form Start date: Tuesday March 5 2019
www.aifinanceinstitute.com
69 Charlton St, New York, NY 10014
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