Machine Learning: Considerations for Fairly and Transparently Expanding Acces...QuantUniversity
Machine Learning: Considerations for Fairly and Transparently Expanding Access to Credit
With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
Markowitz portfolio optimization is optimal in theory, however, when applied in practice it often fails catastrophically. Usually, this is addressed by various simplifications to increase robustness. In this talk I will make the case that the reason this theory fails in practice is because uncertainty in the parameter estimation is not taken into account. By using Bayesian statistics we can fix Markowitz and retain all its desirable properties while still having a robustness technique that can be easily extended. This talk is geared at intermediate and will give a general introduction to Bayesian modeling using PyMC3 and focus on application and code examples rather than theory.
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Generating Synthetic Data with Generative Adversarial Networks: Opportunities and Challenges
Limited data access continues to be a barrier to data-driven product development. In this talk, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge.
We identify key challenges of existing GAN approaches with respect to fidelity (e.g., capturing complex multidimensional correlations, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity).
To address fidelity challenges, we discuss our experiences designing a custom workflow called DoppelGANger and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DoppelGANger achieves up to 43% better fidelity than baseline models.
With respect to privacy, we identify fundamental challenges with both classical notions of privacy as well as recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges.
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
QU Speaker Series - Session 3
https://qusummerschool.splashthat.com
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...QuantUniversity
Machine Learning: Considerations for Fairly and Transparently Expanding Access to Credit
With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
Markowitz portfolio optimization is optimal in theory, however, when applied in practice it often fails catastrophically. Usually, this is addressed by various simplifications to increase robustness. In this talk I will make the case that the reason this theory fails in practice is because uncertainty in the parameter estimation is not taken into account. By using Bayesian statistics we can fix Markowitz and retain all its desirable properties while still having a robustness technique that can be easily extended. This talk is geared at intermediate and will give a general introduction to Bayesian modeling using PyMC3 and focus on application and code examples rather than theory.
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Generating Synthetic Data with Generative Adversarial Networks: Opportunities and Challenges
Limited data access continues to be a barrier to data-driven product development. In this talk, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge.
We identify key challenges of existing GAN approaches with respect to fidelity (e.g., capturing complex multidimensional correlations, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity).
To address fidelity challenges, we discuss our experiences designing a custom workflow called DoppelGANger and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DoppelGANger achieves up to 43% better fidelity than baseline models.
With respect to privacy, we identify fundamental challenges with both classical notions of privacy as well as recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges.
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
QU Speaker Series - Session 3
https://qusummerschool.splashthat.com
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
Qu speaker series 14: Synthetic Data Generation in FinanceQuantUniversity
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance
Robotics & Artificial (RAI) Intelligence webinar: Law & Regulation for RAI In...KTN
The Robotics & AI Innovation Network hosted a webinar addressing some of the legal and regulatory issues faced by the RAI community in the UK. Three legal experts provided their expertise to address these issues.
- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
Machine Learning and AI: An Intuitive Introduction - CFA Institute MasterclassQuantUniversity
Learn how artificial intelligence (AI) and machine learning are revolutionizing financial services — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by financial firms, to augment traditional investment decision making.
This overview session offers a tour of machine learning and AI methods, examining case studies to understand the technology companies, data vendors, banks, and fintech startups that are the key players in trading and investment management. Practical examples and case studies will help participants understand key machine learning methodologies, choose an algorithm for a specific goal, and recognize when to use machine learning and AI techniques
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
QuantUniversity Machine Learning in Finance CourseQuantUniversity
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
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Rapid prototyping quant research ml models using the qu sandboxQuantUniversity
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Machine Learning in Finance: 10 Things You Need to Know in 2021QuantUniversity
Machine Learning and AI has revolutionized Finance! In the last five years, innovations in computing, technology and business models have created multiple products and services in Fintech prompting organizations to prioritize their data and AI strategies. What will 2021 bring and how should you prepare for it? Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
Modular Machine Learning for Model ValidationQuantUniversity
Topic: Modular Machine Learning for Model Validation
Implementing model validation through a set of interdependent modules that utilizes both traditional econometrics and data science techniques can produce robust assessments of the predictive effectiveness of investment signals in an economically intuitive manner.
The proposed methodology, modular machine learning, also answers a number of practical questions that arise when applying block time series cross-validation such as what number of folds to use and what block size to use between folds.
It is possible to re-interpret the Fundamental Law of Active Management into a model validation framework by expressing its fundamental concepts, information coefficient and breadth, using the formal language of data science.
In this talk, we introduce an approach towards model validation which we call modular machine learning (MML) and use it to build a methodology that can be applied to the evaluation of investment signals within the conceptual scheme provided by the FL. Our framework is modular in two respects: (1) It is comprised of independent computational components, each using the output of another as its input, and (2) It is characterized by the distinct role played by traditional econometric and date science methodologies.
Qu speaker series 14: Synthetic Data Generation in FinanceQuantUniversity
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance
Robotics & Artificial (RAI) Intelligence webinar: Law & Regulation for RAI In...KTN
The Robotics & AI Innovation Network hosted a webinar addressing some of the legal and regulatory issues faced by the RAI community in the UK. Three legal experts provided their expertise to address these issues.
- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
Machine Learning and AI: An Intuitive Introduction - CFA Institute MasterclassQuantUniversity
Learn how artificial intelligence (AI) and machine learning are revolutionizing financial services — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by financial firms, to augment traditional investment decision making.
This overview session offers a tour of machine learning and AI methods, examining case studies to understand the technology companies, data vendors, banks, and fintech startups that are the key players in trading and investment management. Practical examples and case studies will help participants understand key machine learning methodologies, choose an algorithm for a specific goal, and recognize when to use machine learning and AI techniques
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
QuantUniversity Machine Learning in Finance CourseQuantUniversity
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
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Rapid prototyping quant research ml models using the qu sandboxQuantUniversity
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Machine Learning in Finance: 10 Things You Need to Know in 2021QuantUniversity
Machine Learning and AI has revolutionized Finance! In the last five years, innovations in computing, technology and business models have created multiple products and services in Fintech prompting organizations to prioritize their data and AI strategies. What will 2021 bring and how should you prepare for it? Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
Modular Machine Learning for Model ValidationQuantUniversity
Topic: Modular Machine Learning for Model Validation
Implementing model validation through a set of interdependent modules that utilizes both traditional econometrics and data science techniques can produce robust assessments of the predictive effectiveness of investment signals in an economically intuitive manner.
The proposed methodology, modular machine learning, also answers a number of practical questions that arise when applying block time series cross-validation such as what number of folds to use and what block size to use between folds.
It is possible to re-interpret the Fundamental Law of Active Management into a model validation framework by expressing its fundamental concepts, information coefficient and breadth, using the formal language of data science.
In this talk, we introduce an approach towards model validation which we call modular machine learning (MML) and use it to build a methodology that can be applied to the evaluation of investment signals within the conceptual scheme provided by the FL. Our framework is modular in two respects: (1) It is comprised of independent computational components, each using the output of another as its input, and (2) It is characterized by the distinct role played by traditional econometric and date science methodologies.
SOP or Personal Statement for University of Hertfordshireaziznitham
Personal statement or SOP is the statement of own individuals stuff. This is the only for your understanding how to write SOP or personal statement and what should be mentioned in it. On the basis of this general SOP, you can make your own.
One who belongs to "Computer Science and Engineering" or "Software Engineering" background can write similar but with own words.
Predicting College STEM Enrollment using HPCC Systems in Educational ResearchHPCC Systems
As part of the 2018 HPCC Systems Summit Community Day event:
Up first, Shah Muhummad Hamdi, Georgia State University, briefly discusses his poster, Dimensionality Reduction on Pbblas.
Following, Itauma Itauma presents his breakout session in the Machine Learning track.
In this study, multiple regression analysis is used to determine if high school students’ perception of their science self-efficacy, identity, and utility predict consideration to enroll in a college stem major. The HPCC Systems ML library will be used to build a multiple regression model utilizing secondary data analysis of the United States High School Longitudinal Study of 2009 (HSLS:09) dataset. The HSLS:09 is a national cohort study of over 23,000 ninth graders from 944 schools in 2009 through their secondary and post-secondary years including choices of college majors and careers. This study demonstrates the use of the HPCC Systems ML library for statistical modeling in education.
Itauma Itauma is a doctoral candidate at Keiser University, a computer science instructor at Wayne State University and an online instructor at Southern New Hampshire University. His interests lie in learning analytics and utilizing HPCC Systems for educational research. He has an undergraduate degree in Electrical Engineering from the University of Ilorin and two Masters Degrees, a Master of Science in Computer Engineering from Istanbul Technical University, majoring in human-robot interaction and a Master of Science in Computer Science from Wayne State University where his thesis was based on leveraging HPCC Systems for Big Data analytics.
Personal statement or SOP is the statement of own individuals stuff. This is the only for your understanding how to write SOP or personal statement and what should be mentioned in it. On the basis of this general SOP, you can make your own.
One who belongs to "Computer Science and Engineering" or "Software Engineering" background can write similar but with own words.
Privacy-Preserving Machine Learning: secure user data without sacrificing mod...Manning Publications
Privacy Preserving Machine Learning is a comprehensive introduction to data privacy in machine learning. Based on years of DARPA-funded cybersecurity research, the book is filled with lightbulb moments that will change the way you think about algorithm design. You’ll learn how to apply privacy-enhancing techniques to common machine learning tasks, and experiment with source code fresh from the latest academic papers.
Learn more about the book here: http://mng.bz/go5Z
SOP or Personal Statement for London South Bank Universityaziznitham
Personal statement or SOP is the statement of own individuals stuff. This is the only for your understanding how to write SOP or personal statement and what should be mentioned in it. On the basis of this general SOP, you can make your own.
One who belongs to "Computer Science and Engineering" or "Software Engineering" background can write similar but with own words.
Technology University for the Future | Plaksha UniversityPlaksha University
Plaksha University aims to reimagine technology education and research for India and the world.It has been founded by a global community of 60+ tech entrepreneurs, business leaders and academicians. Theyhave come together to create the next generation of fearless leaders who will solve the world's most complexchallenges.
Uniform Legal Framework for AI: The EU AI Act establishes a uniform legal framework for the development, marketing, and use of artificial intelligence systems within the EU, aimed at promoting trustworthy and human-centric AI while ensuring a high level of health, safety, and fundamental rights protection.
Risk-Based Approach: The regulation adopts a risk-based approach, classifying AI systems based on the level of risk they pose, from minimal to unacceptable risk, with stringent requirements for high-risk AI systems, particularly those impacting health, safety, and fundamental rights.
Prohibitions for Certain AI Practices: Unacceptable risk practices, such as manipulative social scoring and real-time biometric identification in public spaces without justification, are prohibited to protect individual rights and freedoms.
Mandatory Requirements for High-Risk AI Systems: High-risk AI systems must comply with mandatory requirements before they can be marketed, put into service, or used within the EU. These requirements include transparency, data governance, technical documentation, and human oversight to ensure safety and compliance with fundamental rights.
Conformity Assessment and Compliance: Providers of high-risk AI systems must undergo a conformity assessment procedure to demonstrate compliance with the mandatory requirements. This includes maintaining technical documentation and conducting risk management activities.
Transparency Obligations: AI systems must be transparent, providing users with information about the AI system's capabilities, limitations, and the purpose for which it is intended, ensuring informed use of AI technologies.
Market Surveillance: The EU AI Act establishes mechanisms for market surveillance to monitor and enforce compliance, with the European Artificial Intelligence Board (EAIB) playing a central role in coordinating activities across member states.
Protection of Fundamental Rights: The Act emphasizes the protection of fundamental rights, including privacy, non-discrimination, and consumer rights, with specific provisions to safeguard these rights in the context of AI use.
Innovation and SME Support: The regulation aims to foster innovation and support small and medium-sized enterprises (SMEs) through regulatory sandboxes and by reducing administrative burdens for low and minimal risk AI applications.
Global Impact and Alignment: While the EU AI Act directly applies to the EU market, its global impact is significant, influencing international standards and practices in AI development and use. Financial industry professionals worldwide should be aware of these regulations as they may affect global operations and international collaborations.
The financial industry is witnessing an emerging trend of Large Language Models (LLMs) applications to improve operational efficiency. This article, based on a round table discussion hosted by TruEra and QuantUniversity in New York in May 2023, explores the potential use cases of LLMs in financial institutions (FIs), the risks to consider, approaches to manage these risks, and the implications for people, skills, and ways of working. Frontline personnel from Data and Analytics/AI teams, Model Risk, Data Management, and other roles from fifteen financial institutions devoted over two hours to discussing the LLM opportunities within their industry, as well as strategies for mitigating associated risks.
The discussions revealed a preference for discriminative use cases over generative ones, with a focus on information retrieval and operational automation. The necessity for a human-in-the-loop was emphasized, along with a detailed discourse on risks and their mitigation.
PYTHON AND DATA SCIENCE FOR INVESTMENT PROFESSIONALSQuantUniversity
Join CFA Institute and QuantUniversity for an information session about the upcoming CFA Institute Professional Learning course: Python and Data Science for Investment professionals.
Learn how artificial intelligence (AI) and machine learning are revolutionizing industries — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by firms, to augment traditional decision making.
https://quforindia.splashthat.com/
Mathematical Finance & Financial Data Science Seminar
AI and machine learning are entering every aspect of our life. Marketing, autonomous driving, personalization, computer vision, finance, wearables, travel are all benefiting from the advances in AI in the last decade. As more and more AI applications are being deployed in enterprises, concerns are growing about potential "AI accidents" and the misuse of AI. With increased complexity, some are questioning whether the models actually work! As the debate about fairness, bias, and privacy grow, there is increased attention to understanding how the models work and whether the models are thoroughly tested and designed to address potential issues.
The area "Responsible AI" is fast emerging and becoming an important aspect of the adoption of machine learning and AI products in the enterprise. Companies are now incorporating formal ethics reviews, model validation exercises, and independent algorithmic auditing to ensure that the adoption of AI is transparent and has gone through formal validation phases.
In this talk, Sri will introduce Algorithmic auditing and discuss why Algorithmic auditing will be a formal process industries using AI will need. Sri will also discuss the emerging risks in the adoption of AI and discuss how QuSandbox, his company is building, will address the emerging needs of formal Algorithmic auditing practices in enterprises.
Seeing what a gan cannot generate: paper reviewQuantUniversity
Seeing what a GAN cannot Generate Paper review: Bau, David et al. “Seeing What a GAN Cannot Generate.” 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019): 4501-4510.
With Alternative Data becoming more and more popular in the industry, quants are eager to adopt them into their investment processes. However, with a plethora of options, API standards, trying and evaluating datasets is a major hindrance to adoption of datasets.
Join Yaacov, Sri, James and Brad discuss the opportunities, pitfalls and challenges of Alternative Data and its adoption in finance
A Unified Framework for Model Explanation
Ian Covert, University of Washington
Explainable AI is becoming increasingly important, but the field is evolving rapidly and requires better organizing principles to remain manageable for researchers and practitioners. In this talk, Ian will discuss a new paper that unifies a large portion of the literature using a simple idea: simulating feature removal. The new class of "removal-based explanations" describes 20+ existing methods (e.g., LIME, SHAP) and reveals underlying links with psychology, game theory and information theory.
Practical examples will be presented and available on the Qu.Academy site
Reference:
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, Su-In Lee
https://arxiv.org/abs/2011.14878
Machine Learning Interpretability -
Self-Explanatory Models: Interpretability, Diagnostics and Simplification
With Agus Sudjianto, Wells Fargo
The deep neural networks (DNNs) have achieved great success in learning complex patterns with strong predictive power, but they are often thought of as "black box"models without a sufficient level of transparency and interpretability. It is important to demystify the DNNs with rigorous mathematics and practical tools, especially when they are used for mission-critical applications. This talk aims to unwrap the black box of deep ReLU networks through exact local linear representation, which utilizes the activation pattern and disentangles the complex network into an equivalent set of local linear models (LLMs). We develop a convenient LLM-based toolkit for interpretability, diagnostics, and simplification of a pre-trained deep ReLU network. We propose the local linear profile plot and other visualization methods for interpretation and diagnostics, and an effective merging strategy for network simplification. The proposed methods are demonstrated by simulation examples, benchmark datasets, and a real case study in credit risk assessment. The paper that will be presented in this talk can be found here.
Qu speaker series:Ethical Use of AI in Financial MarketsQuantUniversity
As AI and ML penetrates the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan will focus on how the AI can be operationalized to help industry professionals and executive teams alike think about opportunities, risks as well as required actions factoring in ethics in our data-driven world.
The world has changed in the last six months with COVID-19! There have been a shakeup in business models and funding. As companies and customers change their behaviors, we are seeing changes on how companies are addressing new challenges.
Join Fintech experts, D.Shahrawat and Sarah Biller for a not to be missed conversation on Fintech in the Post-Covid age
Master Class: GANS with Applications in Synthetic Data GenerationQuantUniversity
Join QuantUniversity for a complimentary fall speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Master Class: GANS with applications in Synthetic data generation
With various innovations in neural networks, GANs are becoming popular as a means of generating synthetic data.
In this master class, Gautier will discuss Generative Adversarial Networks (GANs) and discuss applications in synthetic data generation and other quantitative finance applications. He will also discuss his work on CORRGANS, Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks.[1]
Reference:
1. https://arxiv.org/abs/1910.09504
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Frontiers in Alternative Data : Techniques and Use CasesQuantUniversity
QuantUniversity Summer School 2020 (https://qusummerschool.splashthat.com/)
https://quspeakerseries10.splashthat.com/
Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
- Risks associated with alternative data
- Processes required to develop signals
- Valuation of alternative data
Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
-Deciphering Federal Reserve communications
- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
- Saving "alpha" with transaction cost analysis
- Using Bloomberg News data to trade FX
Qwafafew meeting: A Sector Rotation Strategy that Beats the Market Handily Es...QuantUniversity
We study a sector rotation strategy that switches among equity sectors, and from equities to bonds, based on signals of a high volatility regime in equities. We find that an implementation of the strategy using highly liquid sector-specific ETFs would have earned 6.6% more than the S&P 500 per year during the period Dec-1998 to Jul-2020, while experiencing much lower volatility. The performance of the strategy is especially strong during crisis periods such as the 1998-2002 crash and recession, the 2008-09 Great Recession, and the current Covid-19 Recession, with much higher and smoother returns than the S&P 500.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
1. Qu Speaker Series
AI Explainability and Model Risk Management
Dr.Anupam Datta
Truera & Carnegie Mellon University
2021 Copyright QuantUniversity LLC.
Hosted By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.quantuniversity.com
02/10/2021
Qu.Academy
https://quspeakerseries22.splashthat.com/
2. 2
QuantUniversity
• Boston-based Data Science, Quant
Finance and Machine Learning
training and consulting advisory
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Building a platform for AI
and Machine Learning Exploration
and Experimentation
5. 5
• Anupam Datta is Professor of Electrical &
Computer Engineering and Computer
Science at CMU and Co-Founder of Truera.
• Anupam is passionate about enabling
responsible adoption of artificial
intelligence. He has led groundbreaking
research in the areas of AI explainability
and governance as well as privacy and
data protection.
• Anupam obtained PhD and MS degrees
from Stanford University and a BTech from
the Indian Institute of Technology,
Kharagpur, all in Computer Science.
Dr.Anupam Datta
9. 9
Instructions for the Lab:
1. Go to https://academy.qusandbox.com/#/register and register using the code:
"QUWINTERSCHOOL"
10. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.
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