Full Machine Learning Week 7-Track Agenda 2023

Predictive Analytics World

June 18-22, 2023 l Red Rock Casino Resort & Spa, Las Vegas


See the full 7-track agenda for the five co-located conferences at Machine Learning Week below. A Machine Learning Week Ticket is required for full access. To view the agenda for one individual conference, click here: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare or Deep Learning World.

Session Levels:

Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level

Sessions pertaining to GENERATIVE AI have been labelled as such.

All times are Pacific Daylight Time (PDT/UTC-7)

Workshops - Sunday, June 18th, 2023

8:30 am
Room: Sienna
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.

Sunday, June 18, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full-day: 8:30am – 4:30pm PDT

Intended Audience: People who want to use R to make predictions and discover valuable relationships in their data.
Knowledge Level: An introductory knowledge of R and machine learning is helpful, but not required.

Workshop Description

R offers a wide variety of machine learning (ML) functions, each of which works in a slightly different way. This one-day, hands-on workshop starts with ML basics and takes you step-by-step through increasingly complex modeling styles. This workshop makes ML modeling easier through the use of packages that standardize the way the various functions work. When finished, you should be able to use R to apply the most popular and effective machine learning models to make predictions and assess the likely accuracy of those predictions.

The instructor will guide attendees on hands-on execution with R, covering:

  • A brief introduction to R’s tidyverse functions, including a comparison of the caret and parsnip packages
  • Pre-processing data
  • Selecting variables
  • Partitioning data for model development and validation
  • Setting model training controls
  • Developing predictive models using naïve Bayes, classification and regression trees, random forests, gradient boosting machines, and neural networks (more, if time permits)
  • Evaluating model effectiveness using measures of accuracy and visualization
  • Interpreting what “black-box” models are doing internally

Hardware: Bring Your Own Laptop
Each workshop participant is required to bring their laptop

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Instructor

Jared P. Lander, Chief Data Scientist, Lander Analytics

Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York and Government R Conferences, an Adjunct Professor at Columbia Business School, and a Visiting Lecturer at Princeton University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone (now in its second edition), a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world.



Instructor
Jared LanderLander Analytics
Chief Data Scientist
Lander Analytics
4:30 pm
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Workshops - Monday, June 19th, 2023

8:30 am
Room: Red Rock Ballroom A
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).

Monday, June 19, 2023 – Red Rock Casino Resort & Spa, Las Vegas

 Full day: 8:30am – 4:30pm PDT

 Intended Audience: Interested in the fundamentals of modern machine learning techniques.

Knowledge Level: For this introductory-level workshop, it is helpful for attendees to already be familiar with the basics of probability and coding.

Free Book! Each attendee will be reimbursed by the organizers for the cost of buying a copy of Dr Elder’s “Handbook of Statistical Analysis and Data Mining Applications 1st Edition” (up to $50, receipt required).

Companion Workshop: This workshop is the perfect complement for Dr. Elder’s other one-day PAW workshop, “The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them,” although both workshops stand alone and may be taken in either order.

Workshop Description

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).
Predictive analytics has proven capable of generating enormous returns across industries – but, with so many machine learning modeling methods, there are some tough questions that need answering:

  • How do you pick the right one to deliver the greatest impact for your business, as applied over your data?
  • What are the best practices along the way?
  • How do you make it sure it works on new data?

In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will describe the key inner workings of leading machine learning algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to select the method and tool best suited to each predictive analytics project.

Attendees will leave with an understanding of the most popular algorithms, including classical regression, decision trees, nearest neighbors, and neural networks, as well as breakthrough ensemble methods such as bagging, boosting, and random forests.

This workshop will also cover useful ways to visualize, select, reduce, and engineer features – such as principal components and projection pursuit. Most importantly, Dr. Elder reveals how the essential resampling techniques of cross-validation and bootstrapping make your models robust and reliable.

Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, highlighting mistakes to avoid.

If you’d like to become a practitioner of predictive analytics – or if you already are and would like to hone your knowledge across methods and best practices – this workshop is for you.

What you will learn:
  • The tremendous value of learning from data
  • How to create valuable predictive models with machine learning for your business
  • Best Practices, with real-world stories of what happens when things go wrong

Why Attend?

View Dr. Elder describing his course, “The Best of Predictive Analytics,” in this brief video:

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Special offer: Register for both this workshop as well as Dr. Elder’s other one-day PAW workshop, “The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them” (complementary but not required), and also receive his co-authored book Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions.


Instructor

Dr. John Elder, Founder and Chair, Elder Research

John Elder leads America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Washington DC, Maryland, North Carolina, and London. Dr. Elder co-authored books on data miningensembles, and text mining — two of which won book-of-the-year awards. John was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. Dr. Elder is an (occasional) Adjunct Professor of Engineering at UVA, and was named by President Bush to serve 5 years on a panel to guide technology for national security.

Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Red Rock Ballroom D
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.

Monday, June 19, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full-day: 8:30am – 4:30pm PDT

Intended Audience: Managers, decision makers, practitioners, and professionals interested in a broad overview and introduction
Knowledge Level: All levels

Workshop Description

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalizeit. To get to that point, your business must follow a gold standard project management process, one that is holistic across organizational functions and reaches well beyond executing the core number crunching itself.

At this workshop, you will gain a deep understanding of the concepts and methods involved in operationalizing machine learning to deliver business outcomes. This workshop focuses on the elements of a machine learning project that define and scope the business problem, ensure that the result is useful in business terms, and help deliver and operationalize the machine learning outcome. Based on CRISP-DM – the most well known, established industry standard management process for machine learning – this course does not dive into the core machine learning technology itself, but focuses instead on how machine learning must be applied in order to be effective. Attendees will have opportunity to apply what they learn to real-life scenarios.

Key Topics:

  • Apply machine learning to business operations through the structure of CRISP-DM
  • Use decision modeling to understand real-world business problems in a way that allows machine learning to be applied effectively
  • Take a decision-centric and business-focused approach to machine learning projects
  • Evaluate and deploy machine learning results to minimize the gap between analytic insight and business improvement

Coverage of the CRISP-DM Project Management Phases:

  • An overview of CRISP-DM and its basic approach
  • Discuss and demonstrate the importance of decisions in the Business Understanding phase
  • Introduce and teach decision modeling as a way to assess the situation and set goals for the project
  • Discuss decision-centric approach to Data Understanding phase of CRISP-DM
  • Discuss decision-centric approach to Data Preparation and Modeling phase of CRISP-DM
  • Discuss decision-centric approach to Evaluation phase of CRISP-DM
  • Discuss decision-centric approach to Deployment phase of CRISP-DM
  • Brief discussion of technical deployment options
  • Specification of business rules in a decision model to turn predictive analytic into prescriptive one
  • Importance of ongoing decision (not just model) monitoring and management

Learning Objectives:

  • Frame data quality and other data needs in decision-centric terms
  • Evaluate machine learning outputs against decision models to determine business value
  • Use decision models to show how machine learning results can be captured and compared
  • Understand different ways in which machine learning can be used to improve decision-making
  • Read and understand a decision model built using the Decision Model and Notation (DMN) standard
  • Develop basic decision modeling skills for use on machine learning projects
  • Understand how decision modeling complements CRISP-DM as an approach to machine learning
  • Understand technology architecture required for machine learning project deployment
  • Be able to use decision model to frame organizational and process change requirements for machine learning project
  • Understand use of business rules and business rules technology alongside machine learning

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Instructor

James Taylor, CEO, Decision Management Solutions

James Taylor is the CEO of Decision Management Solutions and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.


Instructor
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Room: Red Rock Ballroom G
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning.

Monday, June 19, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full day: 8:30am – 4:30pm

Important note: Each workshop participant is required to bring their own laptop. 
Intended Audience: Anyone who wishes to learn how to create deep learning systems using PyTorch, TensorFlow, Keras, and other popular software libraries.
Knowledge Level: Basic knowledge of machine learning terminology. Minimal programming experience with a C-family language such as Python, C/C++, C# or Java is recommended but not required.

Workshop Description

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning. It’s a hands-on class; you’ll learn to implement and understand both deep neural networks as well as unsupervised techniques using PyTorch, TensorFlow, Keras, and Python. Just as importantly, you’ll learn exactly what types of problems are appropriate for deep learning techniques, and what types of problems are not well suited to deep learning.

The instructors, Prerna and Bardia, take part in applying cutting-edge Large Language Models and other custom models to address various industries’ needs. They will be sharing case studies and examples from their experience during the workshop. Workshop participants will access much of the same state-of-the-art training material used for this work at Microsoft. Along the way, James will cover case studies detailing large-scale deployments for their internal clients that have generated astounding ROIs.

During the day, workshop attendees will gain the following practical hands-on experience:

  • How to prepare, normalize, and encode data for deep learning systems.
  • How to install deep learning libraries including TensorFlow, Keras, and PyTorch, and the pros and cons of each library.
  • How to create deep learning predictive systems for various kinds of data: classical business data, time series data (such as sales data), image data (such as the famous MNIST dataset for handwriting recognition), and text/document data (such as legal contracts). These datasets are a great place to start – however, for the more experienced attendee, even more challenging, “next level” datasets, such as for object recognition, will be optionally available.

This workshop assumes you have a basic knowledge of machine learning terminology but does not assume you are a machine learning expert. Some theory will be presented but only enough to help you understand how to make a practical, working deep learning system. This is a code-based workshop, so some programming experience will be helpful. However, beginners will be able to follow along but may have to work a bit harder to keep up.

Hardware: Bring Your Own Laptop

Important note: Each workshop participant is required to bring their own laptop.

You are encouraged to bring a Windows 10 or 11 laptop if you have one available, but a Mac laptop will work as well.

Details regarding laptop options as well as pre-install instructions for both platforms will be updated closer to the event, since the new, forthcoming PyTorch 2.0, coming out spring 2023, will be utilized — but for now, you can access last year’s details here.

Assistants will also be on hand to help attendees with hardware/software issues.

Attendees receive an electronic copy of the course materials and related code at the conclusion of the workshop.

Schedule

  • Workshop starts at 8:30am
  • Morning Coffee Break at 10:30am – 11:00am
  • Lunch at 12:30pm – 1:15pm
  • Afternoon Coffee Break at 3:00pm – 3:30pm
  • End of the Workshop: 4:30pm

Instructors:

Bardia Beigi, Applied Scientist II, Microsoft 

Bardia Beigi works at Microsoft as an Applied Scientist II in the Industry AI group delivering AI/ML based solutions to various industries within Azure. Bardia has a master’s degree in Computer Science from Stanford University, as well as Bachelor of Applied Science in Engineering Physics from the University of British Columbia. In his spare time, Bardia enjoys traveling, trying out new dessert spots, and learning new life hacks.

Prerna Singh, Applied Scientist II, Microsoft 

Prerna Singh is currently working as an Applied Scientist II in the Industry AI group @Microsoft where she develops machine learning-based solutions for different industrial verticals including finance and sustainability. Before joining Microsoft, she obtained her master’s degree in Electrical and Computer Engineering with a concentration on Machine Learning from Carnegie Mellon University (CMU). Prerna is passionate about machine learning, NLP and deep Reinforcement Learning. Besides work, Prerna enjoys traveling, Zumba and hiking in her free time.

Instructors
Bardia BeigiMicrosoft
Senior Applied Scientist
Microsoft
Prerna SinghMicrosoft
Applied Scientist II
Microsoft
4:30 pm
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Machine Learning Week - Las Vegas - Day 1 - Tuesday, June 20th, 2023

8:00 am
Room: Red Rock Foyer
Registration & Networking Breakfast
Room: Red Rock Foyer
Registration & Networking Breakfast
Room: Red Rock Foyer
Registration and Networking Breakfast
Room: Red Rock Foyer
Registration and Networking Breakfast
Room: Red Rock Foyer
Registration and Networking Breakfast
8:45 am
Room: Red Rock Ballroom B
PAW Business
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
Room: Red Rock Ballroom B
PAW Financial
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
PAW Healthcare
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
Room: Red Rock Ballroom B
PAW Industry 4.0
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
Room: Red Rock Ballroom B
Deep Learning World
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
9:15 am
Room: Red Rock Ballroom B
PAW Business
GENERATIVE AI
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
Room: Red Rock Ballroom B
PAW Financial
GENERATIVE AI
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
Room: Red Rock Ballroom B
PAW Healthcare
GENERATIVE AI
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
Room: Red Rock Ballroom B
PAW Industry 4.0
GENERATIVE AI
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
Room: Red Rock Ballroom B
Deep Learning World
GENERATIVE AI
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
9:40 am
Room: Red Rock Ballroom B
PAW Business
Sponsored by
Google Cloud
Speaker
Juan AcevedoGoogle Cloud
Enterprise Machine Learning Architect
Google Cloud
Room: Red Rock Ballroom B
PAW Financial
Sponsored by
Google Cloud
Speaker
Juan AcevedoGoogle Cloud
Enterprise Machine Learning Architect
Google Cloud
Room: Red Rock Ballroom B
PAW Healthcare
Sponsored by
Google Cloud
Speaker
Juan AcevedoGoogle Cloud
Enterprise Machine Learning Architect
Google Cloud
Room: Red Rock Ballroom B
PAW Industry 4.0
Sponsored by
Google Cloud
Speaker
Juan AcevedoGoogle Cloud
Enterprise Machine Learning Architect
Google Cloud
Room: Red Rock Ballroom B
Deep Learning World
Sponsored by
Google Cloud
Speaker
Juan AcevedoGoogle Cloud
Enterprise Machine Learning Architect
Google Cloud
10:00 am
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
Room: Charleston Ballroom
Morning Breaks and Exhibits
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
10:30 am
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
​ML leadership
Speaker
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
Model metrics
Speaker
Dean AbbottAbbott Analytics
President
Abbott Analytics
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Post-pandemic recovery
Case Study: Royal Caribbean
Speaker
Matt DenesukRoyal Caribbean Group
SVP, Data Analytics & Artificial Intelligence
Royal Caribbean
Room: Red Rock Ballroom G
PAW Financial
Uncertainty estimation
Speaker
Yuanyuan LiMunich Re
Research Scientist
Munich Re
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
Chris FranciskovichOSF Healthcare
Vice President of Advanced Analytics
OSF Healthcare System
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Terry MillerBōwdee
Founder & Director
Bōwdee
Room: Summerlin F
Deep Learning World
Speaker
Michelle LiPaychex
Data Scientist II
Paychex
11:15 am
Short Break
Short Break
Short Break
Short Break
Short Break
11:25 am
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
AI talent
Speaker
Kian KatanforooshWorkera
CEO
Workera.ai
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
Performance metrics
Speaker
Sam KoslowskyHarte Hanks
Senior Statistcial Consultant
Harte Hanks
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Workflow optimization
Case study: Autodesk
Speaker
Marina Petzel
Machine Learning Engineer
Autodesk
Room: Red Rock Ballroom G
PAW Financial
Credit risk
Case Study: MPOWER Financing
Speaker
Mack WallaceMPOWER Financing
Head of Financial Products
MPOWER Financing
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Arnab ChakrabartiHitachi America
Senior Research Scientist
Hitachi America, Ltd.
Room: Summerlin F
Deep Learning World
Speaker
Rishab Ramanathanopenlayer
CTO & Cofounder
Openlayer
12:10 pm
Room: Charleston Ballroom
Lunch - Seating available at The Veranda and Red Rock Terrace
Room: Charleston Ballroom
Lunch - Seating available at The Veranda and Red Rock Terrace
Room: Charleston Ballroom
Lunch & Exhibits - Seating available at The Veranda and Red Rock Terrace
Room: Charleston Ballroom
Lunch and Exhibits - Seating available at The Veranda and Red Rock Terrace
Room: Charleston Ballroom
Lunch & Exhibits - Seating available at The Veranda and Red Rock Terrace
1:30 pm
Room: Red Rock Ballroom B
PAW Business
Speaker
Brandon SouthernAmazon
Former Sr. Manager - Business Intelligence
Amazon
Room: Red Rock Ballroom G
PAW Financial
Speaker
William WIlkinsSafety National Casualty Corporation
VP, Chief Risk and Analytics Officer
Safety National Casualty Corporation
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
Glenn Wasson PhD
Administrator of Analytics
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Marina Petzel
Machine Learning Engineer
Autodesk
Room: Summerlin F
Deep Learning World
Speaker
Chip Huyenclaypot
CTO & Cofounder
Claypot AI
2:15 pm
Room: Red Rock Ballroom B
PAW Business
Sponsored by
Amazon Web Services
Speaker
Cheryl AbundoAmazon Web Services
Principal Solutions Architect
Amazon Web Services
Room: Red Rock Ballroom G
PAW Financial
Sponsored by
Lightsolver
Speaker
Eric Ben-ArtziLightsolver
Head of Financial Solutions
Lightsolver
Short Break
Short Break
Room: Summerlin F
Deep Learning World
Speaker
David Talby Ph.DJohn Snow Labs
Chief Technology Officer
John Snow Labs
2:35 pm
Short Break
Short Break
 
 
Short Break
2:40 pm
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
ML project management
Speakers
Cal Al-DhubaibPandata
CEO & AI Strategist
Pandata
John Shannahan
Director of Cancer Informatics
University Hospitals
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
Recommendation systems
Case study: Albertsons
Speakers
Dao Ho PhDAlbertsons
Senior Data Scientist
Albertsons Companies
Ankita Mangal PhDAlbertsons
Senior Data Scientist
Albertsons Companies
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Case study: Waste Management, Inc.
Speaker
Jodi BlombergWaste Management, Inc.
Senior Director, Data Science and Machine Learning
Waste Management, Inc.
Room: Red Rock Ballroom G
PAW Financial
Risk management
Speaker
Jie ChenWells Fargo
Head of Decision Science and Artificial Intelligence Model Validation
Wells Fargo
Room: Red Rock Ballroom B
PAW Healthcare
Speakers
Cal Al-DhubaibPandata
CEO & AI Strategist
Pandata
John Shannahan
Director of Cancer Informatics
University Hospitals
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Sarah KalicinIntel
Data Scientist
Intel Corporation
Room: Red Rock Ballroom A
Deep Learning World
Speakers
Dao Ho PhDAlbertsons
Senior Data Scientist
Albertsons Companies
Ankita Mangal PhDAlbertsons
Senior Data Scientist
Albertsons Companies
3:00 pm
Short Break
 
 
 
Short Break
3:05 pm
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
Career development
Case study: CVS Health
Speaker
Dave CoughlinCVS
Executive Director: Commercial Sales Analytics
CVS Health
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
Data management
Speakers
Aimee DeGrauweJohn Deere
Group Product Manager - Manufacturing Data Platform and Analytics
John Deere
Justin GoldJohn Deere
Product Manager - Manufacturing Data
John Deere
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Case study: Jetson (ebikes and scooters)
Speakers
Lucas Ribeiro de AbreuDHauz Analytics
Chief Data Scientist
DHAUZ
Guilherme KogaJETSON
VP of Planning and Business Intelligence
Jetson
 
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
Nephi Walton
Associate Medical Director
Intermountain Healthcare
 
Room: Summerlin F
Deep Learning World
Speaker
Huy VoOtrafy Technologies Inc
Data Science and Analytic manager
Otrafy Technologies Inc
3:25 pm
Room: Charleston Ballroom
Exhibits & Afternoon Break
Room: Charleston Ballroom
Exhibits & Afternoon Break
Room: Charleston Ballroom
Exhibits & Afternoon Break
Room: Charleston Ballroom
Afternoon Break and Exhibits
Room: Charleston Ballroom
Exhibits & Afternoon Break
3:55 pm
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
Speakers
Karl RexerRexer Analytics
President
Rexer Analytics
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
ML platforms
3:55 pm - 4:15 pm
Speaker
Diego KlabjanNorthwestern University
Professor
Northwestern University
MLOps
4:20 pm - 4:40 pm
Speaker
Noa GoldmanDagshub
Lead Product Manager
Dagshub
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Analytic operationalization
3:55 pm - 4:15 pm
Case study: Wallick Communities (senior housing)
Speaker
Corwin Smith
Director Business Intelligence and Analytics
Wallick Communities

4:20 pm - 4:40 pm
Case Study: Target
Speaker
Subramanian IyerTarget
Sr. Director, AI
Target
Room: Red Rock Ballroom B
PAW Financial
Speakers
Karl RexerRexer Analytics
President
Rexer Analytics
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
Room: Red Rock Ballroom I
PAW Healthcare
Speakers
Ben ClevelandUnityPoint Health
Principle Data Scientist
UnityPoint Health
Megan ZeislerUnityPoint Health
Data Scientist
UnittyPoint Health
Room: Red Rock Ballrooms A & H
PAW Industry 4.0
3:55 pm - 4:15 pm
Speaker
Diego KlabjanNorthwestern University
Professor
Northwestern University
4:20 pm - 4:40 pm
Speaker
Wes MadrigalMad Consulting
President and ML Engineer
Mad Consulting
Room: Red Rock Ballroom A
Deep Learning World
3:55 pm - 4:15pm
Speaker
Diego KlabjanNorthwestern University
Professor
Northwestern University
4:40 pm
Short Break
Short Break
 
Short Break
 
4:45 pm
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
Sourcing analytics talent
Speaker
Vashishtha DoshiUCLA Anderson School of Management
Manager of Industry Relations
UCLA Anderson School of Management
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
MLOps, edge deployment
Speaker
Kumaran PonnambalamCisco
Principal Engineer - AI
Cisco Systems, Inc
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics

4:45 pm - 5:05 pm
Case study: A Fortune 500 CPG company
Speaker
Jennifer Schaff Ph.D.Elder Research
Vice President of Commercial Services
Elder Research
Getting to deployment
5:10 pm - 5:30 pm
Case study: Utilities, IoT, logistics
Speaker
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Room: Red Rock Ballroom B
PAW Financial
Speaker
Vashishtha DoshiUCLA Anderson School of Management
Manager of Industry Relations
UCLA Anderson School of Management
Room: Red Rock Ballroom I
PAW Healthcare
Speakers
Ben ClevelandUnityPoint Health
Principle Data Scientist
UnityPoint Health
Lauren Rost PhDMayo Clinic
Translational Informatics Analyst
Mayo Clinic
Glenn Wasson PhD
Administrator of Analytics
Room: Red Rock Ballroom B
PAW Industry 4.0
Speaker
Vashishtha DoshiUCLA Anderson School of Management
Manager of Industry Relations
UCLA Anderson School of Management
Room: Summerlin F
Deep Learning World
Speakers
Kian KatanforooshWorkera
CEO
Workera.ai
Rishab Ramanathanopenlayer
CTO & Cofounder
Openlayer
5:30 pm
Room: Charleston Ballroom
Networking Reception in the Exhibit Hall
Room: Charleston Ballroom
Networking Reception in the Exhibit Hall
Room: Charleston Ballroom
Networking Reception in the Exhibit Hall
Room: Charleston Ballroom
Networking Reception in the Exhibit Hall
Room: Charleston Ballroom
Networking Reception in the Exhibit Hall
6:45 pm
Dinner with Friends - sign up in the app
Dinner with Friends - sign up in the app
Dinner with Friends - sign up in the app
Dinner with Friends - sign up in the app
Dinner with Friends - sign up in the app

Machine Learning Week - Las Vegas - Day 2 - Wednesday, June 21st, 2023

8:00 am
Room: Red Rock Foyer
Registration & Networking Breakfast
Registration & Networking Breakfast
Room: Red Rock Foyer
Registration & Networking Breakfast
Room: Red Rock Foyer
Registration and Networking Breakfast
Room: Red Rock Foyer
Registration & Networking Breakfast
8:45 am
Room: Red Rock Ballroom B
PAW Business
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
Room: Red Rock Ballroom B
PAW Financial
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
Chris FranciskovichOSF Healthcare
Vice President of Advanced Analytics
OSF Healthcare System
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Room: Summerlin F
Deep Learning World
Speaker
Pranjal Daga
Product Leader
Brex
8:55 am
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
Andrew Bate
VP & Head, Safety Innovation & Analytics
GSK
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Room: Summerlin F
Deep Learning World
Speaker
Vanja JosifovskiKumo.AI
CEO & Co-Founder
Kumo.AI
9:40 am
Room: Red Rock Ballroom B
PAW Business
Speaker
Evan WimpeyElder Research
Director of Strategic Analytics
Elder Research
Room: Red Rock Ballroom B
PAW Financial
Speaker
Evan WimpeyElder Research
Director of Strategic Analytics
Elder Research
Room: Red Rock Ballroom B
PAW Healthcare
Speaker
Evan WimpeyElder Research
Director of Strategic Analytics
Elder Research
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Karl RexerRexer Analytics
President
Rexer Analytics
Room: Summerlin F
Deep Learning World
Speaker
Christopher BrossmanThe RealReal
VP of Machine Learning
The RealReal
10:00 am
Short Break
 
Short Break
Short Break
Short Break
10:05 am
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
ML ethics
10:05 am - 10:25 am
Speaker
David Talby Ph.DJohn Snow Labs
Chief Technology Officer
John Snow Labs
ML ethics
10:30 am - 10:50 am
Speaker
Joel Atkins
AVP, Data Science
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
Feature engineering
Speaker
Brandon SouthernAmazon
Former Sr. Manager - Business Intelligence
Amazon
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Workforce analytics
Case Study: Bristol Myers Squibb
Speaker
Emma Vazirabadi Ph.D.Bristol-Myers Squibb
Associate Director of People Insights & HR Analytics
Bristol-Myers Squibb
Room: Red Rock Ballroom G
PAW Financial
Insurance underwriting
Speaker
Gordon YangPacific Life
Actuary & Director - Data Science & Advanced Analytics
Pacific Life
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Alejandro Jesús Castañeira RodriguezJANZZ Ltd.
Principal Data Scientist
JANZZ Ltd.
Room: Summerlin F
Deep Learning World
Speaker
10:50 am
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
Room: Charleston Ballroom
Exhibits & Morning Break
Room: Charleston Ballroom
Morning Break and Exhibits
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
11:15 am
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
Speakers
Dean AbbottAbbott Analytics
President
Abbott Analytics
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Karl RexerRexer Analytics
President
Rexer Analytics
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
Advanced methods
Speaker
Aric LaBarrInstitute for Advanced Analytics at NC State University
Associate Professor of Analytics
Institute for Advanced Analytics at NC State University
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Workforce analytics
11:15 am - 11:35 am
Case Study: Loblaw (Canadian Grocery Retailer)
Speaker
Nadeem FazilLoblaw Companies Limited
Director Data Science
Loblaw Companies Limited
ML ethics
11:40 am - 12:00 pm
Case Study: Seyfarth Shaw
Speakers
Eric DunleavyDCI Consulting Group
Vice President of Employment and Litigation Support Services
DCI Consulting Group
Annette TymanSeyfarth Shaw
Partner
Seyfarth Shaw
Room: Red Rock Ballroom G
PAW Financial
Credit risk
Speaker
Rohit AgarwalMobisy
Chief Data Officer
Mobisy Technologies
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
Dave CoughlinCVS
Executive Director: Commercial Sales Analytics
CVS Health
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Jonathan KyleAmazon Web Services
Global Business Development & GTM - Predictive Modeling
Amazon Web Services
Room: Summerlin F
Deep Learning World
GENERATIVE AI
Speaker
Abhishek Sharmadeepmind
Research Engineer
DeepMind
12:00 pm
Room: Charleston Ballroom
Lunch & Exhibits - Seating available at The Veranda and Red Rock Terrace
Room: Charleston Ballroom
Lunch - Seating available at The Veranda and Red Rock Terrace
Room: Charleston Ballroom
Lunch & Exhibits - Seating available at The Veranda and Red Rock Terrace
Room: Charleston Ballroom
Lunch and Exhibits - Seating available at The Veranda and Red Rock Terrace
Room: Charleston Ballroom
Lunch & Exhibits - Seating available at The Veranda and Red Rock Terrace
1:15 pm
Room: Red Rock Ballroom B
PAW Business
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Red Rock Ballroom B
PAW Financial
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
David Talby Ph.DJohn Snow Labs
Chief Technology Officer
John Snow Labs
Room: Red Rock Ballroom B
PAW Industry 4.0
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Summerlin F
Deep Learning World
GENERATIVE AI
Speaker
Sami Ghocheforethought
Cofounder & CTO
Forethought
2:00 pm
Short Break
Short Break
Short Break
Short Break
Short Break
2:05 pm
Room: Red Rock Ballroom B
PAW Business
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Speakers
Cheryl AbundoAmazon Web Services
Principal Solutions Architect
Amazon Web Services
Usha JagannathanMcKinsey & Company
Former Principal Engineer
McKinsey & Company
William Komp
Principal Data Scientist
Komplytics LLC
Jennifer Schaff Ph.D.Elder Research
Vice President of Commercial Services
Elder Research
Room: Red Rock Ballroom B
PAW Financial
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Speakers
Cheryl AbundoAmazon Web Services
Principal Solutions Architect
Amazon Web Services
Usha JagannathanMcKinsey & Company
Former Principal Engineer
McKinsey & Company
William Komp
Principal Data Scientist
Komplytics LLC
Jennifer Schaff Ph.D.Elder Research
Vice President of Commercial Services
Elder Research
 
 
 
2:15 pm
 
 
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
Michael Albert Ph.D.
​Assistant Professor
University of Virginia Darden School of Business
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Ayush PatelTwelvefold
Co-founder
Twelvefold
Room: Summerlin F
Deep Learning World
GENERATIVE AI
Speaker
Devanshi VyasCensius
Co-Founder
Censius
3:00 pm
Room: Charleston Ballroom
Exhibits & Afternoon Break
Room: Charleston Ballroom
Exhibits & Afternoon Break
Room: Charleston Ballroom
Exhibits & Afternoon Break
Room: Charleston Ballroom
Afternoon Break and Exhibits
Room: Charleston Ballroom
Exhibits & Afternoon Break
3:30 pm
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
Analytics strategy
3:30 pm - 3:50 pm
Speaker
Brian Sampsel
Vice President of Analytics Strategy
International Institute for Analytics
ML teams
3:55 pm - 4:15 pm
Speaker
Dan ShieblerAbnormal Security
Head of Machine Learning
Abnormal Security
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
Advanced methods
Speaker
William Komp
Principal Data Scientist
Komplytics LLC
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Speaker
Isaac EspinozaRoot Insurance
Strategy & Reinsurance
Root Insurance
Room: Red Rock Ballroom D
PAW Financial
Insurance Applications
Speaker
Isaac EspinozaRoot Insurance
Strategy & Reinsurance
Root Insurance
Room: Red Rock Ballroom I
PAW Healthcare
Moderator
Chris FranciskovichOSF Healthcare
Vice President of Advanced Analytics
OSF Healthcare System
Speakers
Michael Albert Ph.D.
​Assistant Professor
University of Virginia Darden School of Business
Andrew Bate
VP & Head, Safety Innovation & Analytics
GSK
Room: Red Rock Ballroom H
PAW Industry 4.0
Speaker
Evan WimpeyElder Research
Director of Strategic Analytics
Elder Research
 
4:15 pm
Short Break
Short Break
Short Break
Short Break
 
4:20 pm
Room: Red Rock Ballroom B
PAW Business TRACK 1: BUSINESS - Analytics operationalization & leadership
Gaining buy-in for deployment
Speaker
Michael LawrenceTata Communications
Director, Business Intelligence, Operations & Transformation
Tata Communications
Room: Red Rock Ballroom A
PAW Business TRACK 2: TECH - Advanced ML methods & MLOps
Model explainability
Speaker
Usha JagannathanMcKinsey & Company
Former Principal Engineer
McKinsey & Company
Room: Red Rock Ballroom D
PAW Business TRACK 3: Cross-industry applications & workforce analytics
Case study: GE Aviation
Speaker
Dinakar DeshmukhGE Aviation
VP of Data Science & Analytics
GE Aviation
Room: Red Rock Ballroom G
PAW Financial
Banking applications
Speaker
Natesh ArunachalamFinicity
Lead Data Scientist
Finicity
Room: Red Rock Ballroom I
PAW Healthcare
Speaker
Steve Anderson
VP of Analytics
Outlook Amusement
Room: Red Rock Ballroom H
PAW Industry 4.0
Speakers
Dean AbbottAbbott Analytics
President
Abbott Analytics
Sarah KalicinIntel
Data Scientist
Intel Corporation
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Karl RexerRexer Analytics
President
Rexer Analytics
 
5:05 pm
End of Conference Day Two
End of Conference Day 2
End of Conference Day 2
End of Conference Day 2
End of Conference Day Two
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Workshops - Thursday, June 22nd, 2023

8:30 am
Room: Summerlin D
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general.

Thursday, June 22, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full-day: 8:30am – 4:30pm PDT

Intended Audience: Practitioners who wish to learn how to execute on machine learning with Python.

Knowledge Level: Prior experience programming in any language (for machine learning or otherwise) and fundamental knowledge of machine learning concepts. This workshop can serve as your first experience executing on machine learning hands-on – or, if you already have such experience with a language or platform other than Python, this workshop will serve to facilitate your “lateral move” to Python.

Workshop Description

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general. Python provides a great way for machine learning newcomers to begin their hands-on practice, or for experienced practitioners to augment their growing battery of tools.

Note regarding deep learning. Python’s popularity has recently grown even further since it is the most common way to access leading deep learning solutions such as TensorFlow. Note that this workshop day does not cover deep learning, since it serves first-time users by covering a broader, foundational range of traditional machine learning methods. However, this training does provide helpful groundwork for the “Hands-On Deep Learning in the Cloud” workshop scheduled for later in the same week.

During this full-day training workshop, instructor Clinton Brownley – a data scientist at WhatsApp and formerly Facebook, where he gained extensive experience leading internal machine learning trainings – will take you on your first steps with Python, guiding you through challenging hands-on exercises to employ various machine learning capabilities within Python and apply them on real world datasets.

A comprehensive training. The training agenda covers the end-to-end machine learning process, including loading and preprocessing data, building, tuning, and comparing classification and regression models, making predictions, and reporting on model performance.

Topics include:

  • Data preprocessing
  • Cross-validation
  • Model tuning
  • Model evaluation
  • Regression
  • Classification
  • Ensemble methods

Diverse application areas. This workshop’s hands-on exercises cover various applications of predictive modeling that serve to mitigate harm and save money, including: gambling, hospital readmissions, nefarious actor detection, time to failure, and hotel bookings.

Bring your laptop with Python pre-installed. Workshop participants are required to bring their own laptops for use during this hands-on workshop with Python version ≥3.x installed. The primary libraries for the workshop are pandasscikit-learn, and matplotlib, but specific examples may rely on other libraries, so participants should also install seabornpymc3, and jupyter.

Pre-install instructions. The easiest way to have both a compatible version of Python as well as all these required libraries on your laptop is to install the Anaconda Distribution of Python. Please be sure to do prior to the workshop day.

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Instructor

Clinton Brownley, Data Scientist, WhatsApp

Clinton Brownley, Ph.D., is a data scientist at WhatsApp, where he’s responsible for a variety of analytics projects designed to improve messaging and VoIP calling performance and reliability. Before WhatsApp, Clinton was a data scientist at Facebook, working on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions. As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and data science courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Instructor
Clinton BrownleyTala
Lead Data Scientist
Tala
Room: Summerlin E
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.

Thursday, June 22, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full day: 8:30am – 4:30pm PDT

Intended Audience: Interested in advanced machine learning techniques.

Knowledge Level: For this intermediate-level workshop, it is helpful for attendees to already be familiar with the basics of machine learning methods.

Free Book! Each attendee will be reimbursed by the organizers for the cost of buying a copy of Dr Elder’s “Handbook of Statistical Analysis and Data Mining Applications 1st Edition” (up to $50, receipt required).

Companion Workshop: This workshop is the perfect complement for Dr. Elder’s other one-day PAW workshop, “The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques,” although both workshops stand alone and may be taken in either order.

Workshop Description

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.

This workshop covers:

  • Antidotes: the best practices that overcome the most common flawed practices
  • Intuitive explanations of resampling methods that ensure your models work on new data
  • Practical tips for both the hard and the soft skills that will see your project through to implementation

In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will survey the most advanced analytics tools in the practitioner’s toolkit, with particular emphasis on resampling tools – such as cross-validation and target shuffling (a method to avert p-hacking devised by Dr. Elder) – which reveal the true accuracy of your models.

Workshop topics also include visualization, feature engineering, global optimization, criteria of merit design, ensembles, and “soft” factors that affect success, such as human cognitive biases. Attendees will also leave with an understanding of the inner workings of the most popular algorithms – including regression, decision trees, nearest neighbors, neural networks, bagging, boosting, and random forests.

Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, illuminating the technical material covered.

If you’d like to become a more expert practitioner of predictive analytics, this workshop is for you.

What you will learn:

  • The 12 subtle pitfalls to watch out for on any new project
  • The latest ways to increase the value of predictive models and machine learning for your business
  • How to succeed when your biggest threat is not technology, but people (e.g., resistance to change)

Why Attend?

View Dr. Elder describing his course, “The Deadly Dozen,” in this brief video:

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Special offer: Register for both this workshop as well as Dr. Elder’s other one-day PAW workshop, “The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques” (complementary but not required), and also receive his co-authored book Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions.

Instructor

Dr. John Elder, Founder and Chair, Elder Research

John Elder leads America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Washington DC, Maryland, North Carolina, and London. Dr. Elder co-authored books on data miningensembles, and text mining — two of which won book-of-the-year awards. John was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. Dr. Elder is an (occasional) Adjunct Professor of Engineering at UVA, and was named by President Bush to serve 5 years on a panel to guide technology for national security.


Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Summerlin F
Post Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

Generative AI has taken the world by storm, scaling machine learning to viably generate the written word, images, music, speech, video, and more. To the public, it is by far the most visible deployment of machine learning. To futurists, it is the most human-like. And to industry leaders, it has the widest, most untapped range of potential use cases.

In this workshop, participants will get an introduction to generative AI and its concepts and techniques. The workshop will cover different techniques for image, text, and 3D object generation, and so forth. Participants will also learn how prompts can be used to guide and generate output from generative AI models. Real-world applications of generative AI will be discussed, including image and video synthesis, text generation, and data augmentation. Ethical considerations when working with generative AI, including data privacy, bias, and fairness, will also be covered. Hands-on exercises will provide participants with practical experience using generative AI tools and techniques. By the end of the workshop, participants will have a solid understanding of generative AI and how it can be applied in various domains.

Thursday, June 22, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full-day: 8:30am – 4:30pm PDT

Intended Audience: Interested in generative AI.

Knowledge Level: This intermediate-level workshop is intended for participants with at least some technical background, such as hands-on coding or familiarity with the basics of machine learning methods.

Workshop Description

Generative AI has taken the world by storm, scaling machine learning to viably generate the written word, images, music, speech, video, and more. To the public, it is by far the most visible deployment of machine learning. To futurists, it is the most human-like. And to industry leaders, it has the widest, most untapped range of potential use cases.

In this workshop, participants will get an introduction to generative AI and its concepts and techniques. The workshop will cover different techniques for image, text, and 3D object generation, and so forth. Participants will also learn how prompts can be used to guide and generate output from generative AI models. Real-world applications of generative AI will be discussed, including image and video synthesis, text generation, and data augmentation. Ethical considerations when working with generative AI, including data privacy, bias, and fairness, will also be covered. Hands-on exercises will provide participants with practical experience using generative AI tools and techniques. By the end of the workshop, participants will have a solid understanding of generative AI and how it can be applied in various domains.

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Instructor

Martin Musiol, Generative AI Expert, GenerativeAI.net 

Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a co-founder of generativeAI.net, a lecturer on AI to over 1000 students, and publisher of the newsletter ‘Generative AI: Short & Sweet’. As a Data Science Manager at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage.

Instructor
Martin MusiolGenerativeAI.net
Generative AI Expert
GenerativeAI.net
4:30 pm
End of Post-Conference Training Workshops
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All times are Pacific Daylight Time (PDT/UTC-7)