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Why do we need a course about AI during our High school years?

Integrating AI in high school is increasingly becoming important because AI is shaping the future, and students need to be prepared for a world where AI is integrated into nearly every industry and application that is in use. AI education teaches students how machines learn, encouraging logical reasoning, data-driven decision-making, and ethical considerations. These are skills that enhance problem-solving abilities across subjects, from math to humanities.  AI is further transforming industries, and understanding AI principles prepares students for careers in a variety of fields such as healthcare, technology, finance, and more.

 

  • College Readiness: High school AI education helps students build foundational knowledge for higher education in computer science, data science, and related fields.

  • Promoting Critical Thinking: AI involves problem-solving, algorithmic thinking, and ethical considerations, encouraging students to think critically about technology and its implications.

  • Broadening Accessibility: Introducing AI in high school democratizes access to cutting-edge technologies, ensuring students from diverse backgrounds can engage with AI concepts early.

  • Ethics and Awareness: Early exposure to AI can help students understand the ethical implications of AI systems, such as bias, privacy concerns, and societal impacts.

  • Digital Literacy: AI education enhances digital literacy, equipping students with the skills to navigate and critically evaluate the AI-powered tools and systems they encounter daily.

  • Engaging Learning Experiences: AI-driven projects and interactive tools make learning more engaging, combining theoretical knowledge with practical applications.

What is the objective of this program?

The program is designed to introduce high school students to the fundamentals of artificial intelligence (AI), Generative AI and to the field of Machine Learning. It covers key topics such as AI applications, its fair use & limitations, machine learning algorithms, ethical considerations of AI, foundational concepts of large language models (LLMs), practical use of LLM tools and applications, Python programming, and data-driven problem-solving skills.​​

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The courses in our program, offers a solid foundation in both the theoretical and practical aspects of AI and Machine Learning. Here’s a brief breakdown of what students will be learning:

AI, Generative AI Fundamentals & its Limitations

  • Introduction to Narrow AI vs General AI: Understanding the differences between specialized AI systems and more generalized, adaptable systems.

  • Real-world AI applications: Exploring how AI is being applied in various industries.

  • Implications and limitations of AI: Discussing ethical concerns, biases in AI, and the potential risks involved in its widespread use.

Machine Learning & Deep Learning

  • Core ML concepts: Supervised learning, unsupervised learning, and Reinforcement Learning (RL): Practical examples of these techniques.

  • Neural Networks: Understanding how neural networks function and are used in various AI applications.

  • Generative AI & Large Language Models (LLMs): Diving into how models like GPT work and their potential for generating text, images, and more.

  • Deep Learning fundamentals: Explaining how deep learning models are structured and how they power many modern AI systems.

Ethics & Responsible AI

  • A strong emphasis on using AI ethically, ensuring students are not only technically capable but also aware of the societal and ethical considerations.

Technical Requirements

  • Students need a Wi-Fi-enabled computer capable of connecting to a cloud-based platform for interactive and hands-on learning.

 

This combination of theoretical grounding and practical exposure to cutting-edge technology is aimed to provide a well-rounded introductory experience for both first-time and advanced users.

What are the pre-requisites for this program?

To be successful in the programs, the following prerequisites are typically recommended although not required. Students will be introduced to foundational Python programming skills as part of the curriculum. The students benefits learning hands on coding on a low-code/no-code ML platform that makes it easier for individuals without deep technical expertise in machine learning to build and train and analyze AI models.  When required, The instructor will provide comprehensive guidance and online resources to support the learning process and ensure a solid understanding of the material.

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  • Basic Mathematical Foundational Concepts: A good grasp of linear algebra, basic calculus, and probability/statistics is beneficial for comprehending algorithms and models.

  • Basic Programming Knowledge: Basic exposure to Python coding is highly encouraged (but not required), including familiarity with libraries like NumPy and pandas. Understanding loops, conditionals, and functions is essential.

  • Logical Thinking and Problem-Solving Skills: The ability to analyze problems systematically and develop logical solutions is key.

 

If you're new to any of these areas, introductory courses or resources in Python programming and foundational math can help you prepare. Please email us at info@9to12ai.com to understand more about the pre-requisites.

How long is the program?

Our Summer Programs are designed for flexibility, allowing students to choose the pace that suits them best. We offer 1-week, 2-week, and weekend options, with each program totaling 15 hours of engaging learning. Each session combines foundational concepts with hands-on, flipped-classroom Python coding exercises—no prior coding experience required. Additionally, students will explore No-Code and Low-Code Machine Learning (ML) platforms, making AI accessible to learners of all skill levels.

 

Fall Enrichment programs spans across 10 weeks, featuring a 90-minute in-person sessions each week. Each session consists of a concise lecture to introduce the foundational concepts, a flipped classroom component where students engage with preparatory reading materials beforehand, and a hands-on lab session. During the lab, students apply theoretical knowledge to practical tasks, such as coding exercises, fostering an interactive, application-focused learning environment.

This structure is designed to deepen understanding by blending theory with active, practical engagement. Most weeks include a straightforward take-home assignment and preparatory reading for the following session. Students are encouraged to complete these tasks before class to maximize their learning experience.

How can I enroll in the program?

You can enroll using the following link:  PROCEED TO ENROLL 

Will I receive a certificate?

Participants who successfully complete the program will be awarded a certificate of completion. Additionally, students will be encouraged to create a GitHub portfolio showcasing their work from lab sessions, assignments, and projects.

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