artificial intelligence in finance institute€¦ · artificial intelligence in finance institute...

www.aifinanceinstitute.com

Artificial Intelligence in Finance Institute

NEW YORK CITY / MARCH 5 – JUNE 11 2019

NEW YORK CITY / MARCH 5 – JUNE 11 2019


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

www.aifinanceinstitute.com 3

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.


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.


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.


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.


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


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


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


To register, please fax or scan and email the completed booking form to:

E-mail: [email protected]

E-mail: [email protected] / Tel:+1 646 824-1265

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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


69 Charlton St, New York, NY 10014

artificial intelligence in finance institute€¦ · artificial intelligence in finance institute...

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