AI & Machine Learning Education Series (WSU)

NVIDIA, NetApp, and Mark III Systems are hosting an AI/Machine Learning Education Series for WSU and its greater community for Spring 2023. These sessions are virtual and will feature industry experts in Machine Learning, who will dive into current trends around AI/ML via tutorials and hands-on rapid labs designed around practical AI education, delivered remotely via Jupyter Notebooks.

This series will focus specifically on themes around ML and DL that might be of interest to greater WSU, including topics like getting started with ML and DL, anomaly detection with ML, computer/machine vision, getting started with containers and AI, digital twins with AI and NVIDIA Omniverse, and more.

We hope you can join us!

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 a complex machine and take it through the steps of training and evaluating a model to make predictions using ML. Labs include scenarios using ML to predict anomalies and failure scenarios and other related scenarios.

Additional Links:

Kaggle: https://www.kaggle.com/learn

NVIDIA GTC – FREE ONLINE AI Conference: https://www.nvidia.com/gtc

Introduction to Deep Learning and 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.

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.

Introduction to Computer Vision

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.

AI and Machine Learning Education Series: Wichita State University (2023)

AI and Machine Learning Education Series:

Wichita State University (2023)