ML and AI Summer School for Finance Professionals
BACK BY POPULAR DEMAND! Spend your summer expanding your knowledge in ML and AI. Our first offering was so popular, we're bringing this course back and adding a bonus week with a guided hands-on exercise to reinforce your learning! Python knowledge required. Add-on a 3-part Python refresher course to your purchase.
Presented By:
Sri Krishnamurthy, CFA, CAP
Founder, QuantUniversity.com
Lesson Length:
90-minute lessons & labs
9 lessons/one per week
Time: Self-paced
Course Dates:
July 9 - September 3, 2020
Instructor Access concludes September 10, 2020
PRMIA Special Pricing:
Receive a $100 discount at check-out use code: PRMIADISCOUNT100
Prerequisite: Basic Python skills
A FREE tutorial on Python is available on-demand.
Or, add on a 3-part, hands-on Python Refresher.
| About This Course |
MEET OUR TRAINING PARTNER
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This course is being delivered by a PRMIA Training Partner, QuantUniversity, and offer a $100 discount for registrations to this course, use PRMIADISCOUNT100 when registering. All registrations, payments, and course operations will be managed exclusively by QuantUniversity. When registering for the course, you will leave PRMIA.org and use QuantUniversity's registration system, proprietary learning management system, and QuSandbox for labs. See QuantUniversity course policy below.
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This course will include labs and case studies using the QuSandbox. Participants are expected to have fundamental knowledge of Python. A FREE tutorial on Python is via Google, available here. Need additional Python training? Join us for an optional 3-part course, June 18, 25, & July 1. At checkout, add "Just Enough Python for Data Science" to your course subscription.
COURSE DESCRIPTION
Lecture & hands-on lab work!
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 we 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.
Learning Objectives
Upon completion of this course, you will be able to:
- Describe the role of Machine Learning and AI in financial services
- Describe when ML and AI techniques are used
- List the key machine learning methodologies
- Choose an algorithm for a specific goal
- Participate in case studies with fully functional code
How It Works
This course is delivered by QuantUniversity. Shortly before the course start date, registrants are provided with login credentials and a link to the course lecture and labs. Each Tuesday a lesson is launched. Although this is a self-paced course, we highly recommend you attend weekly classes and participate in labs during the week scheduled to get the most out of the course and support from the faculty.
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| Agenda |
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Topic |
Optional Add-on
June 18, 25 & July 1 |
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Python Refresher: "Just Enough Python for Data Science - Financial Professional Edition"
This introductory course, geared towards financial professionals will discuss key concepts needed to write and understand Python. Rather than bogging you with all the syntactical details, we will focus on the key elements and packages giving you just enough orientation to start your Data science journey in Python.
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Lesson 1
July 9, 2020
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Machine Learning and AI: An Intuitive Introduction
- Machine Learning and Statistics
- A Tour of ML and AI Methods
- Key Drivers Influencing the Adoption of ML and AI
- Key Applications
- Key Players
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Lesson 2
July 16, 2020
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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
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Lesson 3
July 23, 2020
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Core Methods and Applications
- Unsupervised Learning
- Dimension reduction and visualizing datasets using PCA, T-SNE
- Manifold Learning
- Case study: Visualizing high-dimensional Datasets
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Lesson 4
July 30, 2020 |
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Clustering, K-means, and more
- Unsupervised Learning
- Clustering techniques, distance measures, K-means, hierarchical clustering, affinity propagation
- Case study 2: Using K-means for automatic clustering of stocks
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Lesson 5
August 6, 2020 |
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Core Methods and Applications
- 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.
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Lesson 6
August 13, 2020 |
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Case Study + Lab
- Neural Networks + Synthetic Data Generation
- Introduction to Neural Networks and Deep Neural Networks
- Case study 4: Synthetic Data Generation for VIX Scenarios
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Lesson 7
August 20, 2020 |
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Working with Text
- Making Sense of Text and Natural Language Processing
- Sentiment Analysis
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Lesson 8
August 27, 2020 |
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Frontier Topics
- Key Issues in Adopting AI and AL into Investment Workflows
- How ML and AI Will Change the Investment Industry
- Frontier Topics
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Lesson 9
September 3 |
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Guided Exercise: Demonstrate Your Skills
Put your newly learned skills to practice while being mentored through the process. Participants will go through a guided exercise Participants will demonstrate their findings to the class and obtain personalized feedback from instructors. |
| 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
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| About Our Experts |
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Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity.com, a data and Quantitative Analysis Company, and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. Prior to starting QuantUniversity, Sri worked at Citigroup, Endeca, MathWorks, and with more than 25 customers in the financial services and energy industries. He has trained more than 1,000 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, an MS in Computer Science, both from Northeastern University, and an MBA with a focus on Investments from Babson College.
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QuantUniversity 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.
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| Continued Risk Learning Credits: 15 |
PRMIA Continued Risk Learning (CRL) programs provide you with the opportunity to formally recognize your professional development, documenting your evolution as a risk professional. Employers can see that you are not static, making you a highly valued, dynamic, and desirable employee. The CRL program is open to all Contributing, Sustaining, and Risk Leader members, providing a convenient and easily accessible way to submit, manage, track and document your activities online through the PRMIA CRL Center. To request CRL credits, please email [email protected].
| Registration |
| Membership Type: |
Public Fee
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PRMIA Network Discount |
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Use discount code ==>> |
PRMIADISCOUNT100 |
| PRMIA Network |
$599 |
$499 |
| with Python Refresher |
$749 |
$649 |
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Register Now
Note: You will leave PRMIA.org and be taken to our training partner's registration site.
Need support? Contact QuantUniversity at [email protected]
QUANTUNIVERSITY REGISTRATION POLICIES
This course is being delivered by a PRMIA Training Partner, QuantUniversity. All registrations, payments, and course operations will be managed exclusively by QuantUniversity. When registering for the course, you will leave PRMIA.org and use QuantUniversity's registration system, proprietary learning management system, and QuSandbox for labs. Questions and requests should be directed to QuantUniversity at [email protected].
Cancellations received up to one week from the course start date (July 30, 2020) will receive a full refund less a $100 processing fee. After this date, refunds or credits will not be issued; the registrant forfeits full payment. This cancellation policy supersedes PRMIA's posted cancellation policy. In the event sufficient registrations are not received, PRMIA and/or QuantUniversity reserve the right to cancel this course one week prior to the start date. Full refunds will be issued if cancelled by the training partner.