Digital Events

Through our digital events, you can remain on the cutting edge of the AI industry from the comfort of anywhere. Top industry leaders will provide updates on the state of AI across various domains.

These digital events are designed for leaders in industry to learn how the present climate is impacting AI initiatives within enterprise organizations.

TUES, May 19, 2020 | 2:00PM EDT

“Ask My Anything” with Reid Blackman, AI Ethics Consultant, Philosophy PhD, and CEO, Virtue

The “Ask My Anything” or “AMA” format is a one-hour opportunity to ask our guest any questions you’d like. Take advantage, and come prepared with questions. Get creative and have fun! Join the Ai4 Slack community below to join this AMA as well as meet other likeminded people who are applying AI to industry.

BIO

Reid Blackman, Ph.D., is the Founder and CEO of Virtue an ethics consultancy. In that capacity he has worked with companies to integrate ethics and ethical risk mitigation into company culture, the development and deployment of emerging tech products, and employer branding. He is also a Senior Advisor to Ernst & Young and sits on their AI Advisory Board, and is a member of IEEE’s Ethically Aligned Design Initiative.  His work has been featured in The Wall Street Journal, he has been quoted in numerous news articles and appeared on Inside Edition, and he regularly speaks at various venues including The World Economic Forum, Cannes Lions, Forbes, NYU Stern School of Business, and Columbia University. Prior to founding Virtue, Reid was a professor of philosophy at Colgate University and the University of North Carolina, Chapel Hill. His research appeared in numerous prestigious professional journals including the European Journal of Philosophy, The Canadian Journal of Philosophy, and Erkenntnis. He also founded a fireworks wholesaling company and was even a flying trapeze instructor. He received his B.A. from Cornell University, his M.A. from Northwestern University, and his Ph.D. from The University of Texas, Austin.

Welcome to Our New 1:1 Meeting Program!

For years, Ai4 has manually set up meetings at our events to help both buyers and sellers achieve their AI goals. With the need to implement AI being greater than ever and no in-person meetings for a while, we’ve decided to do our part to make sure industry’s AI progress doesn’t slow down.

We’ve selected the top AI companies from our community for you to choose from below. These companies are vetted by Ai4, and we’ll do the work to set the video meeting for you. Our goal is to provide you value by narrowing down the vast AI vendor landscape and removing the tedium of the initial outreach to a potential partner. This is a free service for the qualified buyers in our community.

Once you submit the form below with your choices, we’ll get back to you within ten days with times for 20-minute video meetings.

Welcome to our trainings & tutorials! Engage in online learning in an intimate classroom-style setting. These courses range from 5 to 16 hours of total class time spanning 2 to 4 days. With classes capped at 30 each, you can expect an intimate hands-on experience, and plenty of time for Q&A. See full descriptions below.

June 1-4, 2020 | 1:00PM EDT | $829

Introduction to Machine Learning With Apache Spark

Machine Learning (ML) is changing the world. To use ML effectively, one needs to understand the algorithms and how to utilize them. This course provides an introduction into the most popular machine learning algorithms.

We will also use Apache Spark as our ML platform. Apache Spark provides scalable ML platform, that makes it possible to analyze large amount of data.

This course teaches Machine Learning from a practical perspective. In-depth coverage of Math / Stats is beyond the scope of this course.

What you will learn:

Spark ecosystem

Spark ML Library

ML Concepts

Regressions

  • Linear Regression
  • Logistic Regressions

Classifications

  • Naive Bayes
  • SVM
  • Decision Trees
  • Random Forest

Clustering algorithms (K-Means)

Principal Component Analysis (PCA)

Recommendations

Industry Use Cases Covered

Finance

  • Predicting house prices
  • Predicting loan defaults at Prosper
  • Predicting income from customs data

Health care

  • Predicting diabetes outcome

Customer service

  • Predicting customer turnover

Text analytics

  • Spam classification

Travel

  • Predicting Uber demand

Politics

  • Predicting election contributions

Audience

Data scientists, Data analysts, Software Engineers

Skill level

Beginner to Intermediate

Prerequisites
  • Good programming background
  • Familiarity with Python would be a plus, but not required
  • No machine learning knowledge is assumed
  • No Spark knowledge is assumed
Lab environment
  • Cloud based lab environment will be provided to students, no need to install anything on the laptop
Students will need the following
  • A reasonably modern laptop with unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
  • Chrome browser

Sujee Maniyam

Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale.  He teaches and consults in AI (machine learning and deep learning) and Big Data  (Hadoop, Spark, NoSQL) and and Cloud technologies.

He is an open source contributor, author ( ‘Hadoop illuminated‘ and ‘HBase Design Patterns‘)  and speaker at conferences.  He also advises and mentors various companies and organizations.

Speaking : http://elephantscale.com/speaking/
Publications : http://sujee.net/books/

Mark Kerzner

Mark is an experienced, hands-on software architect, practicing and teaching AI, Machine Learning, Blockchain, Spark, Hadoop, NoSQL, and more. He worked in a variety of verticals (Hightech, Healthcare, O&G, Legal, Fintech). His classes are hands-on and draw heavily on his industry experience. Mark is certified in Google Cloud (GCP), Amazon (AWS), and Hadoop.

Mark is an author and maintainer for a popular open source project for lawyers and researchers, FreeEed, which deals with search and massive scalability.

  • Monday, June 1st: 1pm-5pm Eastern
  • Tuesday, June 2nd: 1pm-5pm Eastern
  • Wednesday, June 3rd: 1pm-5pm Eastern
  • Thursday, June 4th: 1pm-5pm Eastern

Spark
  • Spark ecosystem
  • Spark data models
  • Spark ML
Machine Learning (ML) Overview
  • Machine Learning landscape
  • Understanding Deep Learning use cases
  • Understanding AI / Machine Learning / Deep Learning
  • Data and AI
  • AI vocabulary
  • Hardware and software ecosystem
  • Understanding types of Machine Learning (Supervised / Unsupervised / Reinforcement)
ML in Python and Spark
  • Spark ML Overview
  • Introduction to Jupyter notebooks
  • Lab: Working with Jupyter + Python + Spark
  • Lab: Spark ML utilities
Feature Engineering and Exploratory Data Analysis (EDA)
  • Preparing data for ML
  • Statistics Primer
  • Data cleanup
  • Extracting features, enhancing data
  • Visualizing Data
  • Labs:
    • Data cleanup
    • Exploring data
    • Visualizing data
Machine Learning Concepts
  • Training and Testing
  • Gradient Descent
  • Overfitting / Under-fitting
  • Cross validation, bootstrapping
  • Confusion Matrix
  • ROC curve, Area Under Curve (AUC)
Linear regression
  • Linear Regression
  • Errors, Residuals
  • Multiple Linear Regression
  • Evaluating model performance
  • Labs:
    • Use case: House price estimates
Logistic Regression
  • Understanding Logistic Regression
  • Calculating Logistic Regression
  • Evaluating model performance
  • Labs:
    • Credit card application
    • College admissions
Classification: SVM (Supervised Vector Machines)
  • SVM concepts and theory
  • SVM with kernel
  • Labs: -Customer churn data
Classification: Decision Trees & Random Forests
  • Classification and Regression Trees (CART) introduction
  • Decision Tree concepts
  • Pruning trees
  • Gini index
  • Bias Variance Tradeoff
  • Random Forest concepts
  • Random Forests features and examples
  • Labs:
    • Predicting loan defaults
    • Estimating election contributions
Classification: Naive Bayes
  • Naive Bayes theory
  • Running Naive Bayes algorithm
  • Evaluating model performance
  • Lab
    • Spam filtering
Unsupervised Algorithms
  • Overview of unsupervised algorithms
  • Supervised vs. unsupervised
  • Understanding unsupervised algorithms
Unsupervised: Clustering: K-Means
  • Theory behind K-Means
  • Running K-Means algorithm
  • Estimating the performance
  • Labs:
    • Predicting Uber demand
    • Clustering shopping trips
Final workshop (time permitting)
  • This is a group workshop
  • Each group will analyze a couple of real world datasets and run ML algorithms
  • Each group will present their findings to the class
June 8-11, 2020 | 1:00PM EDT | $529 [early bird]

Intro to Deep Learning With TensorFlow & Keras

The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has open sourced a library called TensorFlow which has become the de-facto standard, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration.

This course introduces Deep Learning concepts and TensorFlow and Keras libraries to students.

This course teaches Machine Learning from a practical perspective. In-depth coverage of Math / Stats is beyond the scope of this course.

What you will learn

Deep Learning concepts

TensorFlow and Keras

Create neural networks with Tensorflow and Keras

Learn to use tools like Tensorboard to help with training neural networks

We will build neural networks to solve the following problems

  • Regression
  • Classification
Industry Use Cases Covered

Finance

  • Predicting loan defaults at Prosper
  • Predicting house prices

Health care

  • Predicting diabetes outcome

Customer service

  • Predicting customer turnover

Audience

Developers, Data analysts, data scientists

Skill level

Introductory to Intermediate

Prerequisites
  • Basic knowledge of Python language and Jupyter notebooks is assumed.
    Even if you haven’t done any Python programming, Python is such an easy language to learn quickly. We will provide Python resources.
Lab environment
  • Cloud based lab environment will be provided to students, no need to install anything on the laptop
Students will need the following
  • A reasonably modern laptop with unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
  • Chrome browser
  • SSH client for your platform

Timothy Fox

Timothy Fox is an experienced Big Data Architect and Data Science Consultant, specializing in Machine Learning and Deep Analytics at scale. He has consulted for many companies large and small and has taken his expertise to Europe, the Middle East, and South Asia as well.

Timothy is active in organizing Hadoop, Spark, and Data Science meetups as well as speaking about topics of interest to Big Data professionals.

  • Monday, June 8th: 1pm-5pm Eastern
  • Tuesday, June 9th: 1pm-5pm Eastern
  • Wednesday, June 10th: 1pm-5pm Eastern
  • Thursday, June 11th: 1pm-5pm Eastern

Section 1: Introduction to Deep Learning
  • Understanding Deep Learning use cases
  • Understanding AI / Machine Learning / Deep Learning
  • Data and AI
  • AI vocabulary
  • Hardware and software ecosystem
  • Understanding types of Machine Learning (Supervised / Unsupervised / Reinforcement)
Section 2: Introducing TensorFlow
  • TensorFlow intro
  • TensorFlow features
  • Execution graph
  • TensorFlow on GPU and TPU
  • TensorFlow API
  • Lab: Setting up and Running TensorFlow
Section 3: Introducing Keras
  • Keras Intro
  • Keras concepts (models, layers)
  • Using Keras API
  • Lab
Section 4: Deep Learning Concepts
  • Introducing Perceptrons
  • Linear Perceptrons
  • Activation Functions (Sigmoid, Tanh, Relu, Softmax)
  • Backpropagation
  • Optimizers (Gradient Descent, Adam, RMSProp)
  • Loss functions for regressions and classifications
  • Vanishing/exploding gradient problem
  • Lab: Tensorflow playground
Section 5: Feedforward Network
  • FFNN architecture
  • Input layer, output layer
  • Hidden layers and Deep neural networks
  • Sizing neural networks
  • Lab: Feedforward Neural Networks
Applying neural networks
  • Lab : Solving classification / regression problems with DN
  • Lab : CPU vs GPU benchmark
SUMMER, 2020 – Exact Date tba

An Introduction to Artificial Intelligence and Its Applications

AI is changing the world!  This course introduces Artificial Intelligence and its applications in the industry.

This high-level course is intended for managers and execs. This is an interactive seminar intended to provide business leaders with a foundational understanding to help you drive AI transformation at your company. We will show plenty of demos and interactive videos to showcase AI advances.

  • The evolution of AI
  • AI vocabulary and terminology
  • Where is the cutting edge research in AI is
  • What kind of applications enterprises are building leveraging AI
  • Machine Learning and Deep Learning
  • How to adopt Ai in an enterprise

Executives, Managers, and PMs

Sujee Maniyam

Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale.  He teaches and consults in AI (machine learning and deep learning) and Big Data  (Hadoop, Spark, NoSQL) and and Cloud technologies.

He is an open source contributor, author ( ‘Hadoop illuminated‘ and ‘HBase Design Patterns‘)  and speaker at conferences.  He also advises and mentors various companies and organizations.

Speaking : http://elephantscale.com/speaking/
Publications : http://sujee.net/books/

Mark Kerzner

Mark is an experienced, hands-on software architect, practicing and teaching AI, Machine Learning, Blockchain, Spark, Hadoop, NoSQL, and more. He worked in a variety of verticals (Hightech, Healthcare, O&G, Legal, Fintech). His classes are hands-on and draw heavily on his industry experience. Mark is certified in Google Cloud (GCP), Amazon (AWS), and Hadoop.

Mark is an author and maintainer for a popular open source project for lawyers and researchers, FreeEed, which deals with search and massive scalability.

MAY 27TH: 1:00-3:30PM 
MAY 28TH: 1:00-3:30PM 

MAY 27TH: 1:00-3:30PM 
AI evolution
  • Little history
  • Recent advances in data, hardware
  • GPU vs CPU
  • Cutting edge research and applications
AI use cases
  • Example use cases across various industries
AI terminology
  • Understanding basic vocabulary in AI
MAY 28TH: 1:00-3:30PM 
Machine Learning
  • Overview of ML
  • ML algorithms and applications
Deep Learning
  • Overview of Deep Learning
  • Deep Learning algorithms and applications
AI Software ecosystem
  • Languages
  • Libraries
  • Cloud offerings
How to adopt AI in enterprises
  • Technology stack
  • Assembling an effective team
  • Process
  • Best Practices
NOT WHAT YOU’RE LOOKING FOR? 

We’re launching more classes soon. See a larger list of options and cast your vote now. We’ll email when your course is available.

Thank you to our sponsors who enable us to provide you with this content for free!
MAY 28, 2020 | 1:00PM EDT

NLP in the Enterprise: A Data Scientist Perspective

Natural language processing has come into its own within business, enabling companies to uncover insights, provide more personalized products, and automate time consuming tasks. This digital panel is geared towards a technical audience, those building and deploying NLP within an enterprise. You can expect these NLP experts to share their successes, challenges, and the future of enterprise NLP.

Agenda

1:00pm EDT – 60 minute panel

  • Wes Barlow, Senior Data Scientist, USAA
  • Jennifer Klemisch, Data Scientist, Boeing Test & Evaluation – Advanced Analytics Strategic Leader
  • J.T. Wolohan, Lead A.I Solutions Architect – NLP, Booz Allen Hamilton [Moderator]
  • Victoria Snorovikhina, Data Scientist, Megafon
  • Johann Beukes, CAIO, Levatas

Interested in Speaking?

Submit Your Request to Speak Here

June 2, 2020 | 1:00PM EDT

The State of AI in Banking

A tumultuous first quarter to say the least, but what does it mean for your AI initiatives? Are banks spending less energy pushing their AI initiatives forward? Or is AI and automation more important than ever in this time of economic uncertainty? This Ai4 Digital Event will gather industry leaders to provide you with a clear snapshot of the state of AI in Banking.

Agenda

1:00pm EDT – Panel #1: Inside the Bank: Risk, Automation, Compliance, Talent

  • Anusha Dandapani, VP, Data Science Lead, Barclays
  • Javed Ahmend, Senior Data Scientist, Metis Corporate Training
  • Ruchi Gupta, Vice President, Credit Risk, HSBC
  • Stuart Neilson, Director, Citi

1:45pm EDT – Panel #2: Externally Facing: Customer Engagement, Marketing, Lending

  • Michelle Bonat, Executive Director, Applied AI ML, Innovation, JPMorgan Chase
  • Dimitri Bianco, Associate Director, Santander Bank [Moderator]
  • Mudassir Azeemi, Lead Interaction Designer, Design System, Wells Fargo

Interested in Speaking?

Submit Your Request to Speak Here

June 9, 2020 | 1:00PM EDT

Computer Vision in the Enterprise: A Data Scientist Perspective

Computer vision has come into its own within business, enabling companies to uncover insights, provide more personalized products, and automate time consuming tasks. This digital panel is geared towards a technical audience, those building and deploying CV within an enterprise. You can expect these CV experts to share their successes, challenges, and the future of enterprise computer vision.

Agenda

1:00pm EDT – 60 minute panel

  • Jesse Shanahan, Lead Data Scientist, Booz Allen Hamilton [Moderator]
  • Vladimir Iglovikov, Senior Computer Vision Engineer, Lyft
  • Daeil Kim, Co-founder and CEO, AI. Reverie
  • Abhishek Singh, Senior Machine Learning Scientist and Engineer, Apple

Interested in Speaking?

Submit Your Request to Speak Here

June 11, 2020 | 1:00PM EDT

The State of AI in Marketing

In a noisy digital world, AI is proving a critical tool to help marketers stand out, reach the right costumers, and drive revenue. This digital panel will explore how marketers are adapting their programs to include more AI-driven strategies. Expect these marketing experts to share their successes, challenges, and the future of AI in marketing.

Agenda

1:00pm EDT – 60 minute panel

  • Soumya Donkada, Head of eCommerce (Hair/Sundial) / Co-Founder Emerge, Unilever
  • Mert Bay, Head of Global Marketing Data Science, Uber
  • Ashish Agarwal, Chief Marketing Officer – Care, Humana

Interested in Speaking?

Submit Your Request to Speak Here

June 16, 2020 | 1:00PM EDT

The State of AI in Telecom

A tumultuous first quarter to say the least, but what does it mean for your AI initiatives? Are telecom companies spending less energy pushing their AI initiatives forward? Or is AI and automation more important than ever in this time of economic uncertainty? You can expect these telecom experts to share their successes, challenges, and the future of AI in Telecom.

Agenda

1:00pm EDT – 60 minute panel

  • Speakers TBA

Interested in Speaking?

Submit Your Request to Speak Here