Tutorial E


Agenda	Tutorials

Tutorial E	Demystifying Machine Learning and Artificial Intelligence: From unsupervised learning through deep learning, ethical AI, explainable AI and ChatGPT.
Fees	$250 USD each
Date	Monday – February 24, 2025
Time	8:00 AM – 11:30 AM PT

Overview	Overview What is Machine Learning Types of Machine Learning Hype versus reality Machine Learning Basics Supervised Learning Unsupervised Learning Deep Learning What is it? Neural networks and their different flavors Recurrent neural networks Convolutional neural networks Building a neural network Reinforcement Learning Pros and cons of the different approaches Ethical AI Misuse and bias within the models Biases in Machine Learning and how to identify and try to prevent them Large Language Model bias Explainable AI and how to develop trust in the models. Tools and metrics Fairness in AI When to use and how Advances in Machine Learning and where it is going in the future Chat GPT and other Large Language Models and their limitations Spiking neural networks On-board satellite processing Last year’s GSAW tutorial on Demystifying Machine Learning was very well received. The students were engaged and very interested in the topics and methodology covered. We presented details on applications of Machine Learning and Deep Learning and the students were clearly interested in learning more about this subject since they are beginning to encounter it regularly in their work. This year, we plan to expound on these topics and present more information on the different aspects of Machine Learning, especially Deep Learning, Neural Networks and Explainable AI, which is becoming more popular but equally misunderstood. We have found that there is a lot of misconceptions surrounding these topics so we want to further demystify it for folks that are new to the field as well as for those who may have explored areas of these technologies. The determination of features in the data is critically important to successfully building a model, and we present ways to determine the most useful features and how to measure the performance and accuracy of the chosen approaches. Machine Learning, Deep Learning and Artificial Intelligence are all hot topics due to their potential to extract actionable information from the data. However, biases in the data can lead to incorrect models and results, so we present ways to be aware of these potential problems and how to mitigate them. With the need to build trust in the results of an AI model, we spend time on how to implement Explainable AI to understand the results of the models
Instructor	Joseph Coughlin
Biography	Joseph Coughlin Joe Coughlin is an Associate Director at Aerospace Corporation working on projects to improve the utilization of sensors and their data for Space Domain Awareness (SDA) application and working for the USSF and SpOC Chief Data Offices to define data usage and standards. He has been instrumental in bringing operational analytics and machine learning technologies to data analysis for SDA missions. He received a Master’s in Astrophysical, Planetary and Atmospheric Physics from the University of Colorado.
Description of Intended Audience and Recommended Prerequisites	Tutorial is designed for a non-technical as well as a technical audience. Tutorial is for those interested in learning more about different aspects of Machine Learning and Artificial intelligence, especially as it can apply to ground system and satellite applications. Students should have a desire to learn the details of how Artificial Intelligence can be implemented for data exploitation and the benefits and pitfalls of the different approaches. This year there will be an added emphasis on how to ensure trust in the models through explainable AI as well as new topics such as ethical AI and the strengths and limitations of language models such as ChatGPT. No prerequisites are needed
What can Attendees Expect to Learn	What the bounds are of what Artificial Intelligence and Deep Learning can realistically do for data exploitation. What is Deep Learning and the different types of Neural Networks and their components. Ethical AI and biases in AI applications and how to consider them in your model. Explainable AI tools and processes and why they are important for building trusted ML system. New areas of AI, such as ChatGPT, and its ramifications for use in government systems.