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QuPodcast

QuPodcast

By Sri Krishnamurthy

QuantUniversity's Podcast on topics in Artificial Intelligence, Machine Learning, Innovation and Quant Finance.
Hosted by Sri Krishnamurthy, CEO and President, QuantUniversity
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Currently playing episode

Dr.Agus Sudjianto : Machine Learning and Model Risk (With a focus on Neural Networks)

QuPodcastAug 01, 2020

00:00
01:05:10
Yaacov Weinstock, James Perkins and Brad Franklin: Alternative Data and the API Jungle

Yaacov Weinstock, James Perkins and Brad Franklin: Alternative Data and the API Jungle

Check out the upcoming speakers at: https://quwinterschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fe3762099aa4a24691da924

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: Alternative Data and the API Jungle

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.

Dec 23, 202001:01:24
Machine Learning in Finance: Fireside Chat with Dr. Matthew Dixon, Dr. Igor Halperin, Dr. Paul Bilokon, and Sri Krishnamurthy

Machine Learning in Finance: Fireside Chat with Dr. Matthew Dixon, Dr. Igor Halperin, Dr. Paul Bilokon, and Sri Krishnamurthy

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f3c39ff99aa4a24691da5f3

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: Fireside Chat with Dr. Matthew Dixon, Dr. Igor Halperin, Dr. Paul Bilokon, and Sri Krishnamurthy

Dec 18, 202035:41
Machine Learning in Finance: Dr Paul Bilokon - Stochastic filtering and MCMC in finance

Machine Learning in Finance: Dr Paul Bilokon - Stochastic filtering and MCMC in finance

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f3c39ff99aa4a24691da5f3

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: Stochastic filtering and MCMC in finance

While the focus in machine learning is on neural networks, we shall demonstrate that other algorithms can be considered in this paradigm. In particular, we shall introduce stochastic filtering and MCMC with applications to finance.

Dec 18, 202027:40
Dr. Igor Halperin: Machine Learning in Finance - Reinforcement Learning and Inverse Reinforcement Learning: Simple Examples and Applications in Finance

Dr. Igor Halperin: Machine Learning in Finance - Reinforcement Learning and Inverse Reinforcement Learning: Simple Examples and Applications in Finance

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f3c39ff99aa4a24691da5f3

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: Reinforcement Learning and Inverse Reinforcement Learning: simple examples and applications in Finance

This talk will introduce Reinforcement Learning (RL) and its Inverse (IRL) and illustrate how they work on very simple simulated experiments. I will then give a short overview of applications of RL and IRL for quantitative finance.

Dec 17, 202031:51
Dr. Matthew Dixon: Machine Learning in Finance - Deep Learning and Equity Portfolio Modeling

Dr. Matthew Dixon: Machine Learning in Finance - Deep Learning and Equity Portfolio Modeling

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f3c39ff99aa4a24691da5f3

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: Deep Learning and Equity Portfolio Modeling

This lab session shall demonstrate how deep learning can be used to model equity factors, commonly used in asset management. The emphasis shall be on interpretability, the ability of deep learning to capture non-linearities, and understanding the differences between linear models.

Dec 17, 202043:17
Saeed Amen & Alex Denev: Frontiers in Alternative Data-Techniques and Use Cases

Saeed Amen & Alex Denev: Frontiers in Alternative Data-Techniques and Use Cases

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f6a21f299aa4a24691da797

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: Frontiers in Alternative Data-Techniques  and Use Cases

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

Dec 17, 202001:18:55
Ian Covert: A Unified Framework for Model Explanation

Ian Covert: A Unified Framework for Model Explanation

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fd964e599aa4a24691da8fb

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: A Unified Framework for Model Explanation

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

Dec 17, 202001:39:44
Dr. Giulia Fanti: Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger

Dr. Giulia Fanti: Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f29eb1699aa4a24691da53a

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 (GANs)

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.

Dec 16, 202001:05:10
Tony Guida: Machine Learning for Factor Investing

Tony Guida: Machine Learning for Factor Investing

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f33ad2a99aa4a24691da597

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 for Factor Investing

In this presentation, Tony first introduced the concept of supervised learning. Then he covered the practitioners angle for constructing non linear multi factor signals using stock characteristics. He showed 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"

available at:

http://www.mlfactor.com/

Dec 16, 202001:02:16
Stefan Jansen: Synthetic Data Generation in Finance

Stefan Jansen: Synthetic Data Generation in Finance

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fb54e3499aa4a24691da86c

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: Synthetic Data Generation in Finance

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.

Reference:
1. 2019 NeurIPS Time-Series GAN paper by Jinsung Yoon, et al.

Dec 15, 202059:18
Practical Issues in Asset Management, A conversation with Dr.Reha Tutuncu, Point 72

Practical Issues in Asset Management, A conversation with Dr.Reha Tutuncu, Point 72

Check out the upcoming speakers at: https://quspeakerseries.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast or on Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f062b8455fd05416e7fd285

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: Practical Issues in Asset Management

The markets have seen significant volatility in the past few months. With the Factor investing boom of the nineties to the whiplash we have seen during #Covid19, Quants and asset management institutions are questioning accepted best practices in asset management.

In this talk Dr.Reha Tutuncu from Point 72 will share his expertise and thoughts on the challenges and issues in Asset management from a practitioner's perspective. Reha will discuss issues associated with Factor investing and multi-period models and discuss how investors should strategize in the day of Covid19

Dec 15, 202001:04:56
Dr. Agus Sudjianto: Machine Learning Interpretability - Self-Explanatory Models: Interpretability, Diagnostics and Simplification

Dr. Agus Sudjianto: Machine Learning Interpretability - Self-Explanatory Models: Interpretability, Diagnostics and Simplification

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fd0ff0e99aa4a24691da8d2

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 Interpretability -  Self-Explanatory Models: Interpretability, Diagnostics and Simplification

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.

Dec 15, 202001:03:16
Dr.Valeria Sadovykh, Dr.Reid Blackman & Jun Wu: Responsible AI in Action

Dr.Valeria Sadovykh, Dr.Reid Blackman & Jun Wu: Responsible AI in Action

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fc8293199aa4a24691da8c1

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: Responsible AI in Action

As the discussion on AI ethics and adoption of Responsible AI grows, there is confusion of what does Responsible AI actually mean for an enterprise. Is it regulation? Is it having a moral stance? Is it policy? Is it to prevent bad actors? As we delegate more and more decision making to machines, we need to not only bring policy, but also have pragmatic ways to adopt these practices within the entrprise. Join industry leaders Dr. Valeria Sadovykh, Dr. Reid Blackman, and Jun Wu on a discussion on what it means to adopt Responsible AI in the enterprise.

Dec 15, 202059:24
Dan Liebau: Ethical Use of AI in Financial Markets

Dan Liebau: Ethical Use of AI in Financial Markets

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fad64f599aa4a24691da862

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: Ethical Use of AI in Financial Markets

As AI and ML penetrate the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan focused 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.

Dec 15, 202057:33
D Shahrawat, & Sarah Biller: Fintech in the Post-Covid Age

D Shahrawat, & Sarah Biller: Fintech in the Post-Covid Age

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fa42ceb99aa4a24691da856

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: Fintech in the Post-Covid Age

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 Biler for a not to be missed conversation on Fintech in the Post-Covid age

Dec 15, 202001:01:39
Gautier Marti: Master Class: GANS with Applications in Synthetic Data Generation

Gautier Marti: Master Class: GANS with Applications in Synthetic Data Generation

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f9834e199aa4a24691da81e

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: GANS with Apps in Synthetic data

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

Dec 15, 202001:30:58
Dr. Jorg Kientz & Ben Steiner: Validation and Machine Learning - Some thoughts on Deep Neural Networks

Dr. Jorg Kientz & Ben Steiner: Validation and Machine Learning - Some thoughts on Deep Neural Networks

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f58f95999aa4a24691da778

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: Validation and Machine Learning - Some thoughts on Deep Neural Networks

Lecture 1: Dr.Jorg Kientz

In this talk we outline the use of Machine Learning algorithms and their potential application. We focus on Deep Neural Networks. The aim is to outline different network architectures. Then, we wish to find a way of choosing the architecture that best fits from a model validation perspective. This approach is illustrated with examples.

Lecture 2: Ben Steiner

In this talk we will discuss the challenges of Deep learning and focus on the key aspects of Model Risk Management for Deep Learning and Alpha Strategies

Dec 15, 202001:37:05
Jennifer Jordan, Kareem Saleh, Anthony Habayeb, & Slater Victoroff: Explainable AI and Bias in Machine Learning: A Financial Industry perspective

Jennifer Jordan, Kareem Saleh, Anthony Habayeb, & Slater Victoroff: Explainable AI and Bias in Machine Learning: A Financial Industry perspective

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f4fe5bb99aa4a24691da76e

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: Explainable AI and Bias in Machine Learning: A Financial Industry perspective

In the last year or so, there has been a significant interest in AI explainability, fairness, and bias. Many regulatory efforts are being proposed to reign in the uncontrolled deployment of AI. Companies on the other hand are grappling with complex black boxes and are figuring out how to build models that are explainable, fair, and bias-free. Many startups are working on interesting technologies to address these issues. In this session, we will discuss AI explainability and Bias from an entrepreneur and investor perspective. and have a discussion on what the opportunities and challenges are and what the future looks like for explainable AI

Dec 15, 202001:10:25
Stu Kozola: Managing Machine Learning Models in the Financial Industry

Stu Kozola: Managing Machine Learning Models in the Financial Industry

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f467c5499aa4a24691da707

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: 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.

Lecture 2: Rapid Prototyping Quant Research ML Models for Algorithmic Auditing using the QuSandbox
Unlike traditional quant models, ML models require constant iteration, tweaking, testing, monitoring and retuning. Without a rigorous process for facilitating these Agile workflows for machine learning, Quants are destined to be tied up in a brittle process that is not agile nor scalable OR build models without any process encumbrances incurring major model risks in their workflow.As the scale of ML model adoption increases within the enterprise, a controlled process that enables Quants to be creative and explore tools and datasets of their choice is needed. In this talk, we will illustrate, through a case study on why a Sandbox based approach to building machine learning models is warranted.

Dec 15, 202001:10:05
Dr.Agus Sudjianto : Machine Learning and Model Risk (With a focus on Neural Networks)

Dr.Agus Sudjianto : Machine Learning and Model Risk (With a focus on Neural Networks)

Check out the upcoming speakers at: https://qusummerschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/library?tagId=5f06326c55fd05416e7fd28a

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.

Aug 01, 202001:05:10
Dr.Joseph Simonian : Modular Machine Learning for Model Validation

Dr.Joseph Simonian : Modular Machine Learning for Model Validation

Check out the upcoming speakers at: https://qusummerschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or 

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/library?tagId=5f06326c55fd05416e7fd28a

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.

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.

Jul 25, 202001:03:41
QuPodcast Trailer
Apr 26, 202000:21