NAVIGATE THE AI WORLD WITH NAVI

What is AI?

Basic AI Concepts

• Patterns and predictions
• Training AI: simple activities to show how AI gets "smarter" with more data.

AI Art

• Create art using AI

Robotics

• How is AI different from humans and robots
• How is AI used in space

What students will do

• Build simple AI models
• Build simple AI applications
• Digital assistants and how computers understand spoken words

No Prerequisites

What is AI?

How does it work?

How does AI impact daily life?

What are the ethical considerations?

Machine Learning, Deep Learning, and Generative AI Basics. 

Students can choose could by creating a custom AI using their own data. Build an interface to this AI in the form of a chatbot. Students can choose text, tabular or image data to build this. 

Students may take AI 1.0 before the camp begins for an additional fee.

Prerequisites AI 1.0

This AI Learning Lab empowers students to Navigate their Future by building on foundational AI concepts and modern applications and developments in AI.  

Students will learn

Basic python

Key algorithms

Neural networks

KNN

Random Forrest

Variables

Loops Conditionals

They will develop familiarity with basic data types such as strings, lists, and dictionaries, enabling them to store, manipulate, and organize data in various forms.

They will complete a project based on what they learned.

Prerequisites: AI 1.5

This program guides students through a progressive journey to master AI concepts and skills. 

Students will learn

 Architecture and functionality of neural networks

Layers, weights, and activation functions 

As they advance, students explore key AI techniques, including generative. The importance of high-quality data and preprocessing is emphasized, ensuring students grasp the critical role of data in developing effective AI systems.

Students will complete a final project based on what they learn.

Prerequisites: AI 2.0

This program guides students through a progressive journey to master AI concepts and skills. It begins with foundational programming, introducing core concepts such as variables, loops, conditionals, and functions, alongside basic data types like strings, lists, and dictionaries. 

Students will further explore the architecture and functionality of neural networks, exploring layers, weights, and activation functions to understand how these systems process information.

As they advance, students explore key AI techniques, including supervised and unsupervised learning, and to analyze how these methods are applied in real-world scenarios. The importance of high-quality data and preprocessing is emphasized, ensuring students grasp the critical role of data in developing effective AI systems. 

Prerequisites: Completion of 2.5 equivalent experience in foundational AI techniques, programming, and neural network architecture. 

Students should also have completed AP stats.

This advanced course is designed for students ready to shape the future through deep exploration and hands-on experience with cutting-edge AI techniques and applications. The curriculum emphasizes critical thinking, innovation, and ethical considerations as students advance their technical expertise.

 Students will explore specialized neural network architectures, such as convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequence data, gaining hands-on experience with deep learning frameworks like TensorFlow or PyTorch. They will also dive into advanced optimization techniques, including backpropagation and fine-tuning gradient descent algorithms.

In addition, students will develop expertise in natural language processing (NLP), learning to analyze and build AI systems that process and generate human language. 

Topics include tokenization, sentiment analysis, and language translation, with practical implementation of transformers, the foundation of models like GPT, for text-based applications. Reinforcement learning (RL) will be another key area of focus, as students investigate how AI learns through trial and error to make decisions in dynamic environments, applying these concepts to gaming strategies and resource optimization.