Full Machine Learning Week 7-Track Agenda 2023

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.

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.

• 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 mining, ensembles, 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.

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.

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.