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About the NOAA AI Workshop Tutorial Sessions

The Tutorial sessions at the NOAA Artificial Intelligence Workshop will provide a hands-on experience with Artificial Intelligence and its applications.

Sign-up

Registration will open on a rolling basis as tutorial details become available.

Contact:

For more information, please contact: katherine.lukens@noaa.gov

Title:
Tutorial on Video and Image Analytics for Marine Environments (VIAME), a Do-It-Yourself AI Toolkit
Instructor(s):
Matthew Dawkins, Anthony Hoogs
Organization:
Kitware
Description:

Recent developments in the collection of large-volume optical survey data by autonomous underwater vehicles (AUVs), stationary camera arrays, towed vehicles, satellites, and other platforms have made it possible for marine scientists to generate size-structured abundance estimates for different species of marine organisms and other objects of interest. However, the immense volume of data collected by such survey methods quickly exceeds manual processing capacity and creates a strong need for automatic image analysis. To address these challenges, we have created an open-source, permissively-licensed toolkit, Video and Image Analytics for Marine Environments (VIAME), which provides a suite of AI capabilities that are customizable by end-users, without any programming, to a wide variety of image and video analytics.

In this session we will first introduce VIAME and its capabilities, followed by a hands-on software tutorial in which participants will use VIAME to solve analytics problems on their own data. Topics to be covered include video annotation, object detection, image-level classification, object tracking, object detector customization and generation, image registration, size measurement, depth-map generation, and detector evaluation. Though not required, for the hands-on portion of the tutorial it is recommended that participants bring a laptop with the software pre-downloaded (see https://www.viametoolkit.org/ for instructions) and samples of their own image or video data exhibiting an information extraction task.

Technology requirements:

User:

Minimal background needed, if possible participants should use their own data with the software pre-installed.

Tech:

The tutorial will cover both desktop and web versions of the software. The desktop version requires Windows or Linux, the web version requires an internet connection and a browser.

Software Installation Guidance:

There are two versions of the VIAME application: Web and Desktop. A browser is all that is needed to run the Web application. Users can register for free and should create their own usernames for the Web application. For the Desktop application, please download the prepared binaries for your specific operating system available on the VIAME GitHub website. The binaries will be downloaded as a .zip file. After extracting all binaries in an installation directory, you should be able to run the GUI. To test whether you have installed the software correctly, try to run:

examples/annotation_and_visualization/launch_annotation_interface.bat

It will take a few minutes to start up and run the first time you do this. For a complete list of Desktop software installation instructions, see the User's Quick-Start Guide on the VIAME GitHub website.

Notes:

Important Notes on Desktop binaries:

1. For the hands-on portion of this tutorial related to the Desktop application, it is recommended that you install the Desktop binaries on a non-government issued computer. Government-issued computers will require the modification of specific IT permissions subject to your own branch/agency. If you only have access to a government-issued computer and wish to participate in the Desktop application portion of this tutorial, we recommend that you submit a ticket to your IT department as soon as possible to request help with the software installation.

2. Desktop application binaries are not available for Mac. However, if you only have access to a Mac, you can still participate in the hands-on portion of this tutorial related to the Web application, which should provide a near-complete experience.

Recording:
Session9_Tutorial_VIAME_20200922.mp4, (MP4, 178.54 MB)
Title:
Learning the Fundamentals of Machine Learning through Forecasting El Niño
Instructor(s):
Ankur Mahesh (ClimateAI), Karthik Kashinath (LBL)
Organization:
ClimateAI & LBL
Description:

El Niño is a cycle of warm and cold temperatures in the equatorial Pacific Ocean. In this tutorial, we will train machine learning algorithms to forecast El Niño at lead times of 1-6 months. We will explore the following machine learning questions:

  • How should data be split into a train set and test set to ensure rigorous evaluation of the machine learning model?
  • Does older data serve as a good training set?
  • Do standard preprocessing techniques in computer science (e.g., normalization) work well with climate datasets?
  • How does one use machine learning to forecast El Niño?
  • How does the performance of different machine learning algorithms compare to one another?

This tutorial is delivered in the form of a Colab notebook which demonstrates the above principles through "fill-in-the-blank" code exercises, visualization questions, and open-ended coding assignments. The notebook includes step-by-step explanations of machine learning concepts and custom scaffold code to assist with data downloading, loading, and formatting.

Technology requirements:

User:

Attendees should be familiar with Python, matplotlib, netCDF or xarray, and pandas. Additionally, we recommend that attendees be familiar with fundamentals of neural networks, linear regression, and train/test splits. In this tutorial, we assume that attendees are familiar with the core concepts of these machine learning algorithms, and we focus on applying these algorithms to climate science.

Tech:

The tutorial requires each person to have access to a fast internet connection. All code will be run on Google Colab, a cloud-based system for running Jupyter notebooks. Ahead of time, please confirm that you can run the following set-up code on Google Colab:

  1. Navigate to this page;
  2. Press "Open in Colab" at the top of the notebook;
  3. In the Colab window, select "Edit > Notebook settings > Hardware Accelerator > GPU;
  4. Run the code cells, which set up the programming environment and load the necessary data;
  5. If you have any issues with these two cells, please email ankur.mahesh@berkeley.edu with the subject line 'NOAA AI Workshop Setup'.
Notes:
Please confirm ahead of time that your Google Colab environment is functioning correctly. Should you experience any issues, please see Technical Requirements item #5.
Title:
A Practical Introduction to Deep Learning for the Earth System Sciences, using PyTorch
Instructor(s):
David Hall
Organization:
NVIDIA
Description:

In this tutorial, we will learn how to build deep learning applications from the ground up using PyTorch. We will begin simply and build toward full-fledged solutions for detecting tropical cyclones and other strong storms in model data and satellite observations. The primary goal of this tutorial is to familiarize you with each of the concepts and tools needed to begin building your own deep learning applications. Previous experience with Python and Numpy are desired, but not required.

Technology requirements:

User:

Familiarity with Python and Numpy are desired, but not required.

Tech:

The tutorial will include both seminar and hands-on activities. All hands-on activies will run on Google Colab. Therefore, this tutorial requires each person to have an active Google account and access to a web-browser to use Google Colab. (Chrome browser is preferred for Mac users.)

Users do not need to download or install anything prior to the tutorial session.

Register:
Registration limited to 200 participants
Title:
Traditional Machine Learning Pipeline Applied to NWP Model Data
Instructor(s):
Amanda Burke
Organization:
University of Oklahoma
Description:

The rapid introduction of machine learning models within the atmospheric sciences has benefitted multiple sub-fields, such as model parameterization, predictive modeling, ensemble post-processing and many others. As computing capabilities continue to grow, machine learning will likely become even more important within the atmospheric sciences. This tutorial provides an overview of the machine learning pipeline, covering necessary fundamentals for individuals interested in exploring traditional machine learning models. Using NWP model data, attendees will learn about and apply different supervised learning principles using python. Fundamental applications include data pre-processing, dataset partitioning, feature selection, as well as model tuning, training, and evaluating. This hands-on tutorial will briefly explore linear models and decision trees, with resources to apply the fundamental concepts to a range of other machine learning models.

Technology requirements:

User:

Prior knowledge of Python (basic to intermediate experience) is a must.

Tech:

This tutorial is comprised of hands-on exercises. All exercises will run on Google Colab. Therefore, this tutorial requires each participant to have an active Google account and access to a web-browser to use Google Colab.

Users do not need to download or install anything prior to the tutorial session.

Important Note:
Please note that the first 30 participants to register will be accepted into the tutorial session and will receive remote participation information in a separate email closer to the tutorial. All other registrants will be put on a waiting list. If you register and cannot attend, please email the organizers as soon as possible to free your spot for another person.
Register:
Registration limited to 30 participants
 

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