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Course

Machine Learning for Financial Modeling

Ended Apr 22, 2022

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Full course description

 

Machine learning has been a major catalyst for financial decision making with big data. From credit scoring in the financial services industry, to investment models for portfolio and wealth management, to algorithmic trading and risk management. It is well known that machine learning is often used to make basic decisions, such as loan approvals, but the field has advanced substantially in the last few years to deliver a much broader set of capabilities. The goal of this short-course is to equip finance professions with a better understanding of which specific techniques in machine learning are shaping the finance industry.  In particular, we address common questions such as “Why has machine learning replaced statistical modeling?” and “how is machine learning used for portfolio selection?”.  The overall format of the course will be oriented towards finance professionals who are key decision makers in their organizations. Real world applications of machine learning using big data shall be demonstrated in areas such as trading, investment, and risk management.  A key value proposition of this workshop will be providing frameworks to understand how to most effectively apply machine learning to financial workflows across a broad range of business functions and roles, from data capture through to modeling, risk management and reporting.

 

Who is the course for? What will you learn?

  • Financial analysts responsible for areas such as risk management, trading, investment management, retail and commercial banking, loan and payment services
  • Financial managers seeking to improve their ability to evaluate the opportunity and competitive advantages of Machine Learning in finance
  • Entrepreneurs and technical founders who are developing new technologies at the intersection of machine learning and finance

 

Instructors:

Igor Cialenco

Igor Cialenco 

Dr. Cialenco is a Full Professor in the Department of Applied Mathematics at Illinois Institute of Technology. He currently serves as Associate Chair and the Director of Graduate Studies supervising the graduate programs in the Department of Applied Mathematics at Illinois Tech.

His current research interests span across several areas of applied probability and statistics, including Mathematical Finance, Statistical Inference for Stochastic Processes, and Stochastic Control.

Dr. Cialenco is serving as Chair for SIAM Activity Group on Financial Mathematics and Engineering for the period 01/2022-12/2022, and previously served as Program Director. He is also a Managing Editor for the International Journal of Theoretical and Applied Finance (IJTAF), and on Editorial Boards of Applied Mathematical Finance, SIAM Journal on Financial Mathematics (SIFIN), Statistical Inference for Stochastic Processes (SISP), and International Journal of Financial Engineering (JFE).

During his academic career, he has taught and developed courses in Financial Engineering, Stochastic Analysis and Statistics at both graduate and undergraduate levels.

Dr. Cialenco holds a Ph.D. in Applied Mathematics, University of Southern California.

His awards include: College of Science Dean’s Excellence Award for Teaching, Illinois Tech, Fall 2015; College of Science and Letters Dean’s Excellence Award for Research and Scholarship, Illinois Tech 2011; Dissertation Fellowship from College of Letters, Arts and Sciences, USC; The First Prize of the Presidium of the Academy of Sciences of Moldova, 2000.

 

Matthew Dixon, Ph.D, FRM