Sep 
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Problem Solving

Introductions + group problem solving with your peers. Come ready with a top of mind challenge. Turn your biggest challenges into questions for our guest speaker.

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Tue
, 
Mar 
17th
 at 
12:00am

QUANTUNIVERSITY In Partnership With

PRMIA

Presents

 

machine learning and AI for financial professionals

Course duration: 1.5-2 hours/session - 8 weeks

Delivery: Online - through QuAcademy

Number of sessions: 8
Case studies + Labs using the QuSandbox


*Combo offer*

Add the 3-part "Just Enough Python for Data Science in Finance" course for just $150 ($349 value) that can be accessed on-demand


*If you would like an invoice for your payment for reimbursement or related questions on alternative payment methods, please contact info@qusandbox.com

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Video Block #5

Clear your calendar - It's going down! Video Blocks kicks off on May 20th, and you're invited to take part in the festivities. Splash HQ (122 W 26th St) is our meeting spot for a night of fun and excitement. Come one, come all, bring a guest, and hang loose. This is going to be epic!

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Video Block #2

Cohort 1 goes Live: Second week May 2020

Course duration: 1.5 hours/session - 8 weeks

Delivery: Online - through QuAcademy

Number of sessions: 8
Case studies + Labs using the QuSandbox

Last minute offer: Join the class before May 15th and get access to the 6-hr "Python for Data Science" course for FREE!

Details of Python course available here: https://qupython.splashthat.com


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COURSE details

A detailed look at the event. 

The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics. 


You will learn:

  • Role of Machine Learning and AI in Financial services
  • When do we use Machine learning and AI techniques?
  • What are the key machine learning methodologies?
  • How do you choose an algorithm for a specific goal?
  • Practical Case studies with fully functional code

 

Delivery:

  • Session: 1.5-2 hours/session
  • Duration: 8 weeks
  • Case study + Labs using the QuSandbox

 

Who should attend?

  • Fundamental and quantitative analysts, risk and investment professionals, portfolio managers new to data science and machine learning
  • Financial professionals new to data-driven methodologies
  • Machine learning enthusiasts interested in use cases in fintech and financial organizations

 

Prerequisites:
This course incorporates hands-on labs and case studies. Participants are expected to have fundamental knowledge of Python.


Pre-class reading will be sent to all registered participants.


Additional Python resources:

- A FREE tutorial on Python is available here  

- Participants who want additional training in Python can add the "Just Enough Python for Data Science in Finance" course hosted by QuantUniversity for a substantial discount.

Class is On-Demand: Take at your own pace!

Details of the pre-req course available here: https://qupython.splashthat.com

 

Analytics for a cause initiative:

QuantUniversity sponsors scholarships ,valued at $30,000 to their educational offerings to students from eight countries and 12 chapters, participating in the PRMIA - Professional Risk Managers'​ International Association Risk Management Challenge. Additional details about our announcement here: https://www.linkedin.com/pulse/quantuniversity-announces-53-scholarships-valued-risk-sri/

 


 

 

  

Session 1

module 1

Machine Learning and AI: An intuitive Introduction

  • Machine Learning vs Statistics: How has the world changed?
  • A tour of Machine Learning and AI methods
    • Supervised Learning Vs Unsupervised Learning
    • Deep Learning
    • Reinforcement Learning
  • Key drivers influencing the adoption of Machine Learning and AI
    • Big Data, Hardware, Fintech, AI, Alternative Data
  • Key applications
    • Credit risk, Personalization, Predicting risk, Portfolio optimization and selection
  • Key players
    • Technology companies, Data vendors, Banks, Fintech startups

Session 2

MODULE 2

 Exploratory data analysis

  • Exploring and Visualizing large datasets
    • The Visualization zoo
    • A framework to decide how to chart datasets
    • Examples on how to build powerful dashboards
    • Case study 1: Visualizing Categorial, Numerical, Cross-sectional and Time series Financial datasets

Session 3

module 3

Unsupervised Learning

  • Dimension reduction and visualizing datasets using PCA, T-SNE
  • Manifold Learning
  • Case study: Visualizing high-dimensional Datasets

Session 4

module 4

Unsupervised Learning

  • Clustering Techniques
  • Distance measures
  • K-means
  • Hierarchical Clustering
  • Affinity Propagation
  • Case study 2: Using K-means for automatic clustering of stocks

session 5

Module 5

Supervised Learning

Learn from the past: How does Supervised machine learning work?

    • Cross sectional data
    • Time series analysis
    • Regression, Random Forests and Neural Networks
  • Evaluating machine learning algorithms
  • Case study 3: Predicting interest rates and credit risk using Alternative data sets.

session 6

module 6

Neural Networks + Synthetic Data Generation

  • Introduction to Neural Networks and Deep Neural Networks
  • Case study 4: Synthetic Data Generation for VIX Scenarios

session 7

module 7

Natural Language Processing

  • Making sense of Text and Natural Language Processing
  • Sentiment Analysis: How to interpret sentiments and use it in stock selection?
  • Case study 5: Analyzing Earning calls using text analytics

session 8

module 8

Frontier topics

  • Key issues in adopting AI and Machine learning into investment workflows
  • How will Machine Learning and AI change the investment industry
  • Frontier topics
    • Anomaly detection
    • Reinforcement learning
    • Quantum Computing
    • Risk in Machine Learning and AI
    • Model governance, Interpretability and Model Management

Instructor

#Blockchain101AnalyzingCryptocurrenciesusingMachineLearningWor

Course instructor:

Sri Krishnamurthy, CFA

Chief Data Scientist, QuantUniversity

 

Sri Krishnamurthy is the founder of www.quantuniversity.com, a data and Quantitative Analysis Company and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than two decades of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications.


Prior to starting QuantUniversity, Sri has worked at Citigroup, Endeca, MathWorks and with more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics and big data in the industry and at Babson College, Northeastern University and Hult International Business School.


Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA with a focus on Investments from Babson College.

 

Cohort 1 goes Live: Second week May 2020

Course duration: 1.5 hours/session - 8 weeks

Delivery: Online - through QuAcademy

Number of sessions: 8
Case studies + Labs using the QuSandbox

Last minute offer: Join the class before May 15th and get access to the 6-hr "Python for Data Science" course for FREE!

Details of Python course available here: https://qupython.splashthat.com


“Whenever you find yourself on the side of the majority, it is time to pause and reflect.”

instructor

#QuMLInFInance

Attendees

hosted by

QuantUniversity

QuantUniversity (www.quantuniversity.com) is a quantitative analytics and machine learning advisory based in Boston, Massachusetts. QuantUniversity runs various data science and machine learning workshops in Boston, New York, Chicago, San Francisco and online. The company offers an Analytics Certificate Program and the Fintech Certificate program along with multiple workshops in its Explore-Experience-Excel series. Contact us at info@qusandbox.com

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Supported by

PRMIA

The Professional Risk Managers International Association (PRMIA) is a professional organization focused on the "promotion of sound risk management standards and practices globally", and "the integration of practice and theory".It provides certification and credentialing for professional risk managers, as well as other educational programs and resources.

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About US

Stephen D. Cutler Center for Investments and Finance

The Stephen D. Cutler Center for Investments and Finance is provides programs and cutting-edge resources that enrich the student learning experience, support faculty research, and engage our alumni community. We’re committed to furthering Babson’s innovative and practical approach to finance education and enabling industry practitioners, faculty, and students to collaborate and learn from one another.

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Thank You to Our Supporters

Past Attendees

Past Attendees of QuantUniversity workshops include Assette, Baruch College, Bentley College, Bloomberg, BNY Mellon, Boston University, Datacamp, Fidelity, Ford, Goldman Sachs, IBM, J.P. Morgan Chase, MathWorks, Matrix IFS, MIT Lincoln Labs, Morgan Stanley, Nataxis Global, Northeastern University, NYU, Pan Agora, Philips Health, Stevens Institute, T.D. Securities and many more..

Cohort 1 goes Live: Second week May 2020

Course duration: 1.5 hours/session - 8 weeks

Delivery: Online - through QuAcademy

Number of sessions: 8
Case studies + Labs using the QuSandbox

Last minute offer: Join the class before May 15th and get access to the 6-hr "Python for Data Science" course for FREE!

Details of Python course available here: https://qupython.splashthat.com


#Blockchain101AnalyzingCryptocurrenciesusingMachineLearningWor
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RSVP

QuantUniversity Model Risk Management Certificate
$1,548.00

3 Courses leading to the QuantUniversity Model Risk Management Certificate 1. Just enough Python for Data Science 2. AI & Machine Learning for Financial Professionals 3. Model Risk Management for Machine Learning Models Certification

Just enough Python for Data Science (add-on)
$150.00

This is for adding the Just enough Python for Data Science course( https://qupython.splashthat.com/) for participants who are already students of the Machine Learning course and want to add the Python course. Not valid for new students

PYTHON FOR DATA SCIENCE + FOUNDATIONS OF MACHINE LEARNING AND AI FOR FINANCIAL PROFESSIONALS
$749.00

This ticket includes a 8-hr online Python class + the 8-session Machine Learning course The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.

MACHINE LEARNING AND AI FOR FINANCIAL PROFESSIONALS
$599.00

The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.

Special Add-ons