Thursday, Feb 23rd (11am-12pm ET)
Intro to Machine Learning and AI:  The Basics, A Tutorial, and Lab
Speaker: Data Scientist, Mark III

In this session, we’ll cover the basics around what Machine Learning is, look at the different ML techniques and methods, examine what a typical ML project lifecycle looks like, and discuss some of the most commonly used example algorithms. This session will also include a 20-minute rapid mini-workshop via Jupyter Notebook where we’ll use an example dataset from Kaggle and take it through the steps of training and evaluating a model to make predictions using ML.  Examples of labs that we’ll utilize include forecasts using ML for anomaly detection and for classifying biological states.
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Thursday, March 9th (11am-12pm ET)
Intro to Deep Learning: An Introduction to Neural Networks
Speaker: Data Scientist, Mark III

In this session, we’ll cover the basics around what Deep Learning is, look at how it fits within the AI/ML universe, dive into neural networks (including CNNs, LSTMs, and GANs), and walk through a typical Deep Learning project lifecycle. We’ll cap the session with a 20-minute rapid mini-workshop via Jupyter Notebook where we’ll use an example dataset (CIFAR-10) to train and evaluate a neural network model using Keras for computer vision use cases.
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Thursday, March 23rd (11am-12pm ET)
Introduction to Datasets
Speaker: Data Scientist, Mark III

In this session, we’ll cover what datasets for Machine Learning and Deep Learning projects look like and how to find them. This will include highlighting some of the most popular datasets in the community today as well as good sources to download these datasets from. Some brief tips and tricks for cleaning up datasets will be covered and we’ll conclude the session with a mini-workshop and lab showing how to import and interact with datasets in a Jupyter Notebook to collect and prep data for forecasting and predictions.
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Thursday, April 6th (11am-12pm ET)
Intro to Computer Vision and Image Analytics
Speaker: Data Scientist, Mark III

In this session, we’ll cover the basics around what computer vision is, how it works (classification, object detection, segmentation), some of the popular frameworks and models used today, and what some of the practical applications might be in research and industry.  We’ll also walk through what a typical Computer Vision project lifecycle might look like. We’ll cap the session with a 20-minute rapid mini-workshop via Jupyter Notebook where we’ll use code examples to build a CNN image classifier as well as using pretrained model libraries for object detection and image segmentation.
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Thursday, April 20th (11am-12pm ET)
Getting Started with Containers and AI
Speakers: Architects, NVIDIA, Mark III

This session will cover the ML/DL ecosystem of container-powered technologies and the best ways to get started and accelerate your journey in building, training, deploying, and scaling your models.  We’ll touch on NVIDIA NGC, Docker, Kubernetes, Singularity, and other ways to get started and get going quickly. Special focus will be placed on how train models for business analytics use cases and deploy them anywhere where its most impactful and useful for the organization.
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Thursday, May 4th (11am-12pm ET)
Intro to Omniverse & Digital Twins
Speakers: Innovation, Mark III & NVIDIA

In this session, we’ll cover the basics around NVIDIA’s Omniverse platform for 3D Design Collaboration and Simulation and the ecosystem of building Digital Twins.  We’ll touch on not only how to set up and rollout an Omniverse environment, but also how to integrate frameworks, like Modulus (physics simulations) and Isaac (robotics) into Omniverse to visualize your models and research.  Whether you’re in a School of Engineering, Architecture, Natural Sciences, Computer Science, Business, or Data Science, we’ll have something for you in this session.