AI/ML Education Series:

Join Mark III Systems, NVIDIA, and Hewlett Packard Enterprise for a virtual AI Education Series in Spring 2022 for the University of Houston and HPE Data Science Institute communities.  This education series is open to all and will cover a cross section of Artificial Intelligence, Machine Learning, and Deep Learning, starting with basic practical tutorials, labs, ways to get started, and will also feature ways to accelerate and scale research via AI/ML and HPC.

Upcoming Sessions:

Previous Sessions/Replays:

Thursday, Sept 2nd (11am-12pm CT)

 

Intro to Machine Learning and AI:  What is it and why we do we need it?

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.

 

Thursday, Sept 16th (11am-12pm CT)

 

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.

 

Wednesday, Oct 6th (11am-12pm CT)

 

Machine Learning Tools in the Social Sciences (at the HPE DSI)

Speaker: Sebastián Vallejo Vera, Ph.D., University of Houston & Johnny Miller, HPE

We are in an era of data abundance and machine-learning tools are increasingly used to extract meaning from all types of datasets.

In this quick demo, I will briefly showcase how social scientist can take advantage of machine-learning tools in classification tasks that ultimately help us take advantage of the current data availability.

I will provide a step-by-step tutorial on how to take advantage of the computation resources available at the HPE DSI using RoBERTa infrastructure in a text classification task. I will also provide examples of other Natural Language Processing tasks useful to social scientist.

 

Thursday, Oct 21st (11am-12pm CT)

 

Intro to Computer Vision

Speaker: Data Scientist, Mark III

In this session, we'll cover the components of a Convolutional Neural Network (CNN) and talk about how they can be used to address image classification, object detection, and image segmentation use cases.  

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.

 

Thursday, Nov 18th (11am-12pm CT)

 

Introduction to Datasets

Speaker: Data Scientist, Mark III

In this session we will 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.

 

Thursday, Feb 17th (10am-11am CT)

 

Intro to Swarm Learning:  A privacy-preserving, decentralized ML framework

Speaker: Krishna Prasad Shastry, Chief Technologist, HPE AI Strategy

Join HPE’s Krishna Prasad Shastry for a discussion on Swarm Learning and how it fundamentally changes the ML computation paradigm by bringing computing close to the data.

This decentralized ML solution utilizes computing power at or near the distributed data sources with the security of the blockchain. Its privacy-preserving features further open up opportunities for collaboration and monetization models across the organizational boundary.

In its completely decentralized architecture, only learned insights instead of the raw data are shared among collaborating ML peers, which tremendously enhances data security and privacy.

 

 

Thursday, March 31st (11am-12pm CT)

 

Containers, NVIDIA NGC, and How to Get Started and Going with AI

Speaker: Solutions Architect, NVIDIA

This session will focus on the open software stack of AI and its universe of tools for data scientists, researchers, and builders.  The NVIDIA NGC catalog is the hub for GPU-optimized software for deep learning (DL), machine learning (ML), and HPC that accelerates deployment to development workflows so data scientists, developers, and researchers can focus on building models, pipelines, and their research.  This session will also touch on container topics like Docker, Kubernetes, and Singularity for GPU-powered workloads.