Hello DAEN students!

I made an error in my announcement yesterday about AIT 746 (below). I spoke to the professor and confirmed you only need one of the following courses completed successfully: CS 504, AIT 636, AIT 736

If any students would like to adjust their schedules for fall based on that information and have questions about how it will affect their overall progress or course balance, please let me know.

Best Regards,

Mary

Mary Baldwin-Slupe

(She/Her/Hers)

Graduate Academic Advisor – Data Analytics Engineering Program

College of Engineering and Computing – Volgenau School of Engineering

https://analyticsengineering.gmu.edu/academics/advising

Upcoming out of Office Dates: Labor Day – September 2^nd 2024

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Schedule an Academic Advising Appointment

From: Mary A Baldwin
Sent: Wednesday, August 21, 2024 4:59 PM
To: Data Analytics <[log in to unmask]>
Subject: AIT 746 Applied Deep Learning Available in Fall 2024

Hello, DAEN Students!

I know several of you reached out to me with questions about the content of AIT 746. The professor of the course in Fall 2024, Dr. Aisha Sikder Behr, wanted to share the information below about the class and invite students to join. Prerequisites for the class are CS 504, AIT 636, and AIT 736.

Are you interested in pushing the boundaries of artificial intelligence? This fall, we are offering a section in AIT-746 Applied Deep Learning.

This course will equip you with the skills and knowledge to tackle real-world problems using cutting-edge deep learning techniques. You'll delve into the world of Convolutional Neural Networks (CNNs), Reinforcement Learning Models, and Transformer architectures – the foundational element behind Large Language Models (LLMs)!

Here's what you can expect:

Gain hands-on experience building and deploying deep learning models.
Explore the applications of CNNs in image recognition and computer vision.
Learn how Reinforcement Learning models can optimize decision-making for complex tasks.
Understand the power of Transformer models that are revolutionizing natural language processing.

By the end of the course, you'll have a solid foundation in deep learning concepts and the practical skills to apply them to real-world applications. See Course Schedule below to learn more. Don't miss this exciting opportunity to gain a solid understanding of deep learning models, sign up today!

Course Schedule:

Module 1 - Introduction to Deep Learning

Module 2 - Neural Networks

Module 3 - Autoencoders and Convolutional Neural Networks

Module 4 - Recurrent Neural Networks (RNNs)

Module 5 - Using Deep Models for Embeddings

Module 6 - Transformer Architecture

Module 7 - Reinforcement Learning, Key Algorithms, and Responsible AI

Module 8 - Machine Learning Operations

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