深度研发营：构建创新技术全景图

• 开课季节：春、夏 （秋季不开） 
• 上课安排：每周日，太平洋时间5:00–7:00pm
• 课程结构：30小时教学(共15节课 × 每节2小时)  
• 教辅时间：24 by 7 AI 教辅 + Appointments with TA  
• 招生对象：9 - 12年级学生 
• 先修要求：完成广度优先营; 或具备扎实的Python技能, 熟悉主要AI概念和模型

课程AAAC-304 AI高级模型和开发 ： 本课程面向具备编程基础的学生，讲授 Python 精要与数据处理技术，构建广泛应用于计算机视觉与自然语言处理任务的深度学习神经网络模型（ANN、CNN、RNN/LSTM），简要引入 AI 2.0 核心技术，包括 Transformers 架构（BERT/GPT）、多模态学习、强化学习（RL）、检索增强生成（RAG）与自主智能体系统。项目内容精选自 MIT、Stanford、Kaggle 及《Nature》杂志等高端内容，学生将完成 AI 服务器与移动端控制面板的集成开发，提升算法实现与系统部署能力。 

Course AAAC-304: Advanced AI Models and Development

This course is designed for students with prior programming experience. It covers essential Python concepts and data processing techniques, and guides students in building deep learning neural network models (ANN, CNN, RNN/LSTM) widely used in computer vision and natural language processing tasks.

The course also introduces core AI 2.0 technologies, including the Transformer architecture (BERT/GPT), multimodal learning, reinforcement learning (RL), retrieval-augmented generation (RAG), and autonomous agent systems.

Project topics are carefully selected from high-level sources such as MIT, Stanford, Kaggle, and Nature. Students will complete integrated development projects involving AI servers and mobile control panels, strengthening their capabilities in algorithm implementation and system deployment.

• Capstone 顶点项目: 每个课程中，通过完整的开发流程，帮助学生将技术转化为集成项目，给家长和同学演示，铺垫高水平科技赛事

Capstone Project:

In each course, students complete a full-cycle development project that guides them through the entire process—from concept design and technical implementation to system integration and final presentation.

Through the Capstone experience, students transform their technical knowledge into a fully integrated project, present their work to parents and peers, and build a strong foundation for participation in high-level science and technology competitions.

• 完成核心技术模块学习与实战编程
• 开发一个可落地的创新项目（可用于竞赛或研究拓展）
• 免费获得孟博士亲授的“目标竞赛特训”（TCT）课程
• 优秀学生具备申请教学助理（TA）资格
• 自动获得进入 SEF 科研竞赛指导计划的资格
• 有机会获得孟博士暑期科研推荐信与大学申请推荐信

• Complete core technical modules and hands-on programming practice
• Develop a deployable innovation project (suitable for competitions or extended research)
• Receive complimentary access to Dr. Meng’s personally taught Targeted Competition Training (TCT) course
• Outstanding students become eligible to apply for Teaching Assistant (TA) positions
• Automatically qualify for the SEF (Science & Engineering Fair) Research Mentorship Program
• Opportunity to receive Dr. Meng’s summer research recommendation letter and college application recommendation letter

• 开课季节：夏、秋 （春季不开） 
• 上课安排：每周日，太平洋时间5:00–7:00pm
• 课程结构：30小时教学(共15节课 × 每节2小时)  
• 教辅时间：24 by 7 AI 教辅 + Appointments with TA  
• 招生对象：9 - 12年级学生 
• 先修要求：完成广度优先营; 或具备扎实的Python技能, 熟悉主要AI概念和模型

• Offered: Summer and Fall (not offered in Spring)
• Schedule: Every Sunday, 5:00–7:00 PM Pacific Time
• Course Structure: 30 total instructional hours (15 sessions × 2 hours each)
• Academic Support: 24/7 AI-assisted support + scheduled appointments with TAs
• Target Students: Grades 9–12
• Prerequisites: Completion of the Breadth-First Program, or strong proficiency in Python with familiarity in major AI concepts and models

教学模块 AAAC-314：AI系统生态协同与实战开发：本课程以智能控制车辆（RCV）为核心载体，融合人工智能、云计算、移动端客户端开发与物联网技术，完成从硬件组装、系统部署，到高级 AI 与 IoT 集成的完整实战流程。学生将基于 Raspberry Pi 平台，掌握追踪、感应、巡线与避障等核心控制功能，进阶至 AI 服务器开发与移动客户端联调，实现远程控制与多设备协同工作。课程同步引入 AI 2.0 关键技术，包括 Transformers 架构（如 BERT/GPT）、多模态感知、语音识别与导航、图像生成与目标检测等前沿应用，系统提升学生的创新能力与工程落地能力，为参与高水平科技竞赛与未来科研奠定坚实基础。

Instructional Module AAAC-314: AI Systems Ecosystem Integration & Applied Development

This course centers on an Intelligent Remote-Controlled Vehicle (RCV) as its core platform, integrating artificial intelligence, cloud computing, mobile client development, and Internet of Things (IoT) technologies. Students complete a full hands-on development cycle—from hardware assembly and system deployment to advanced AI and IoT integration.

Built on the Raspberry Pi platform, students master essential control capabilities such as tracking, sensing, line-following, and obstacle avoidance. They then progress to AI server development and mobile client integration, enabling remote control and multi-device collaboration.

The course also introduces key AI 2.0 technologies, including Transformer architectures (such as BERT/GPT), multimodal perception, speech recognition and navigation, image generation, and object detection. Through this comprehensive systems-level training, students significantly strengthen their innovation capacity and real-world engineering implementation skills, building a solid foundation for high-level technology competitions and future research endeavors. 

• Capstone 顶点项目: 每个课程中，通过完整的开发流程，帮助学生将技术转化为集成项目，给家长和同学演示，铺垫高水平科技赛事

Capstone Project:

In every course, students complete a full end-to-end development cycle that transforms their technical learning into a fully integrated, working project. From system design and implementation to testing and presentation, students experience a real engineering workflow.

The final project is demonstrated to parents and peers, strengthening students’ confidence and communication skills while laying a strong foundation for participation in high-level science and technology competitions.

• 完成核心技术模块学习与实战编程
• 开发一个可落地的创新项目（可用于竞赛或研究拓展）
• 免费获得孟博士亲授的“目标竞赛特训”（TCT）课程
• 优秀学生具备申请教学助理（TA）资格
• 自动获得进入 SEF 科研竞赛指导计划的资格
• 有机会获得孟博士暑期科研推荐信与大学申请推荐信

• Complete core technical modules and intensive hands-on programming practice
• Develop a real-world, deployable innovation project (suitable for competitions or extended research)
• Receive complimentary access to Dr. Meng’s personally taught Targeted Competition Training (TCT) course
• Outstanding students become eligible to apply for Teaching Assistant (TA) positions
• Automatically qualify for the SEF (Science & Engineering Fair) Research Mentorship Program
• Opportunity to receive Dr. Meng’s Summer Research Recommendation Letter and College Application Recommendation Letter