This project focuses on using audio analysis techniques in combination with a Convolutional Neural Network (CNN) model to detect
emotions in speech. It's designed to provide a framework for understanding and recognizing emotions in spoken language, which has
practical applications in various fields, including human-computer interaction, sentiment analysis, and mental health monitoring.

Streaming has become a popular form of online entertainment with AI streamers recently gaining traction.
In this project, you will combine multiple neural networks to create a virtual streamer who can play games,
interact with the chat, and create engaging content, among other aspects. Ultimately, you will deploy the streamer
to a streaming platform like Twitch.

Large model training demands immense computing power. Federated learning presents an innovative solution by leveraging
distributed devices instead of relying on a single machine. Blockchain's distinct properties form an ideal base for interactions
in this setup. You will learn the basics of deep learning and blockchain to implement federated learning using Ethereum.

We will be developing a sentiment analysis model for detecting whether a movie review is positive or negative.
Specifically, we will be delving into Natural Language Processing (NLP) to construct a sentiment analysis model.
Our primary objective is to create a Neural Network that is capable of ascertaining whether a given movie review carries
a positive or negative sentiment so that we can unravel the intricate emotions embedded within movie reviews

Steganography is used to conceal information within plain sight, while Steganalysis is employed to reveal
this information. You will hide messages within images and then utilize various techniques to detect and unveil
the concealed content. Afterward, you will train a machine learning model to classify images that contain hidden information.

We aim to rigorously evaluate the efficiency of a Large Language Model (LLM) represented by RoBERTa
in addressing a critical classification challenge: the early detection of cancer. Throughout this study, you will gain a
deep understanding of the Transformer Architecture which laid the foundation for all LLMs, and provide insights into addressing
critical real-world challenges, contributing to your knowledge of both machine learning and medical applications.

Decentralized applications have emerged as a transformative force in the world of blockchain technology, enabling transparent, secure,
and censorship-resistant interactions on a global scale. Among the emerging blockchain platforms, Solana stands out for its high-performance
capabilities and scalability, making it a promising foundation for building dApps that can handle complex tasks and accommodate growing
user demands. In this project, we delve into the exciting realm of decentralized applications on the Solana blockchain, exploring their
potential to revolutionize industries and reshape the way we engage with digital services. Through comprehensive analysis and hands-on
development, we aim to uncover Solana's unique features and advantages while addressing challenges and providing insights
into the future landscape of decentralized applications.

To assist forensics experts in identifying what events took place at the scene of a crime, you will
learn the basics of image classification and train a model using convolutional neural networks (CNNs)
to classify bloodstain patterns commonly found in crime scenes into three categories: passive, transfer, and impact.

AI is often seen as less biased than humans, but it can inherit biases from human-curated data,
affecting its decisions. For example, biased hiring practices can lead AI to unfairly rank job resumes
based on names. AI's inner workings are often unclear, creating transparency issues. This project delves
into various AI models and how XAI can unveil their operations. As a team, you'll select a model and a
real-world issue to explore, then use XAI to examine and improve that model.

Humans break down complex tasks into simpler steps, so what if we took the same approach towards
teaching AIs how to navigate their environment? In Scaffold, you will try to answer this question
by studying various reinforcement learning algorithms and applying them to AIs as they learn to navigate
their environment. The AI will be piloting a 3D character in a game engine, so if you have familiarity
with game design or Unity/Unreal (hi ATEC majors), you're an ideal fit!