Hot Presentations on Causal ML, Insights from Regulatory, and Topic Modeling

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Hot Presentations on Causal ML, Insights from Regulatory, and Topic Modeling

Machine Learning Week | June 4-7, Phoenix, Arizona

Less than two weeks remain before we convene in Phoenix for Machine Learning Week. Join us and take advantage of MLW 2024’s program, organized by three track topics:

Track 1: BizML – Business practices for ML operationalization

Track 2: Tech – Advanced ML methods & MLOps

Track 3: ML Use Cases – Cross-industry deployment

Secure a spot

Highlights from the Agenda

Leveraging causal Machine Learning for informed decision-making: a collaborative and iterative approach

In the field of Artificial Intelligence and Machine Learning (AI/ML), a significant challenge lies not only in developing accurate models but also in effectively utilizing them to drive business decisions. This presentation aims to discuss the application of causal ML techniques in influencing important business decisions. By leveraging causal ML, we can uncover the causal relationships between variables, enabling us to understand the impact of various factors on business outcomes. This understanding empowers organizations to make informed decisions and optimize their strategies for success.

Furthermore, this presentation will highlight the importance of engaging business partners from the outset of the AI/ML implementation process. By actively involving them, we gain valuable insights into their existing manual decision-making processes. This understanding allows us to develop an iterative plan that aligns with their needs and preferences, ensuring their comfort and buy-in throughout the implementation journey. Additionally, a rigorous test and learn approach is designed to measure the success of the implemented AI/ML solution, providing tangible evidence of its effectiveness in driving business decisions. This collaborative and iterative approach not only enhances the adoption of AI/ML but also fosters a culture of continuous improvement and data-driven decision-making within organizations.

Identifying Insights and Trends in Regulatory Filings

Regulatory filings from public companies contain a wealth of valuable information, but extracting meaningful insights can be a daunting task due to data quality issues and the sheer volume and complexity of the data. In this session, we will explore how data science techniques can be leveraged to unlock the insights buried within regulatory filings. Through case studies in Financial Services and Utilities, learn how data science techniques help to identify risks, detect anomalies, and extract key themes, sentiment, and competitive intelligence from filings’ narrative sections using NLP. Gain understanding of the end-to-end process from data preprocessing to model deployment. Best practices for interpreting results, communicating findings, and ethical considerations with sensitive financial data will also be covered.

AI Guided Domain-Specific Topic Modeling

A case study will be presented which describes a process for identifying relevant topics to a customer in a specific domain based on a corpus of user comments. We will review in detail how the process is created, how the customer interacts with the process to curate their own labeled data, and how that data is used for downstream analysis and modeling tasks. We will describe how we monitor the results for accuracy and how we manage changes over time. We will show how this process is domain agnostic and easily repeatable.

👉 View the complete agenda

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Free book: Event registrants will receive a copy of MLW Founder Eric Siegel's new book, The AI Playbook, which presents a six-step practice called bizML that ushers ML projects from conception to deployment.

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