Teen Theory's AI Academy

Key Projects:

• Interactive calculator with multiple operations
• Intelligent grade calculator
• Building basic gaming infrastructure

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Session 1: What is AI?

Students discover AI's presence everywhere around them – from Siri responding to voice commands to Netflix suggesting their next binge-watch. Through interactive games distinguishing AI from non-AI systems, students develop an intuitive understanding of artificial intelligence concepts. They learn to identify AI applications in daily life, building foundational knowledge that makes advanced concepts accessible.

Session 2: History and Types of AI

This session takes students on a journey through AI's evolution, from early calculators to modern ChatGPT. Students explore the differences between Narrow AI (what we use today), General AI (human-level intelligence), and the theoretical Strong AI. Through timeline activities and classification exercises, they understand AI's current capabilities and future potential.

Session 3: Introduction to Python

Students write their first lines of code, learning basic Python syntax through immediate practical application. They master variables, basic operations, and simple loops while building a functional calculator. This hands-on approach ensures students gain confidence in programming fundamentals rather than feeling overwhelmed by abstract concepts.

Session 4: Data Types and Conditions in Python

Building on programming basics, students learn to work with different data types and make programs that think logically. Through creating a grade calculator that automatically assigns letter grades, they understand how computers make decisions using if-else conditions. This session bridges the gap between simple scripting and intelligent program behavior.

Session 5: Loops & Functions

Students master the art of making programs repeat tasks and organize code efficiently. By building an engaging game, they learn how loops create dynamic interactions while functions organize code logically. These concepts prepare them for the structured thinking required in AI development.

Session 6: AI Thinking and Flow

This crucial session teaches students to think like AI developers, breaking down complex problems into logical steps. Through creating flowcharts and building a game with simple decision-making logic, students develop computational thinking skills that form the foundation for all future AI learning.

Key Projects (Project themes will vary as per student interests):

• Multi-feature item classification system
• Price predictor with multiple variables
• Intelligent performance clustering analysis

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Session 7: Data and its Importance in AI

Students discover why data is called "the new oil" by exploring real datasets and understanding how AI learns from information. They distinguish between structured data (like spreadsheets) and unstructured data (like photos and text), learning why clean, organized data is crucial for AI success. Through hands-on CSV file exploration, students gain practical experience with the raw material of AI.

Session 8: Introduction to Machine Learning

This pivotal session demystifies machine learning through engaging analogies and practical examples. Students learn the three main types of machine learning – supervised (learning with examples), unsupervised (finding hidden patterns), and reinforcement (learning through trial and error). Interactive activities help students categorize real-world ML applications and understand when to use each approach.

Session 9: AI Tools – Google Colab & scikit-learn

Students get hands-on with professional AI development tools, learning to use Google Colab for coding and scikit-learn for machine learning. They import essential libraries, load datasets, and create their first data visualizations. This session transforms students from AI learners to AI practitioners using industry-standard tools.

Session 10: Supervised Learning - Classification

Students build their first real AI model that can classify items based on features like color, size, and texture. They learn how computers learn to categorize by studying examples with known answers. Through iterative testing and improvement, students understand how AI develops decision-making capabilities and can evaluate model accuracy.

Session 11: Supervised Learning - Regression

Moving beyond categories to numerical predictions, students create a price predictor, such as house pricing, using location, size, and age data. They learn how AI identifies patterns in numerical relationships and makes predictions about unseen data. This session demonstrates AI's practical applications in finance, real estate, and economic forecasting.

Session 12: Unsupervised Learning - Clustering

Students explore AI's ability to find hidden patterns by clustering students based on academic performance across different subjects. Without providing answers, they watch AI discover natural groupings in data. This session reveals how AI can uncover insights humans might miss and applications in market segmentation and pattern discovery.

Key Core AI Projects (Themes will vary as per student interests):

• Multi-text word frequency analyser with visualisation
• Advanced image edge detector with multiple filters
• Strategic Gaming AI with learning capabilities,
• Comprehensive sentiment analyser for social media/ other platforms
• Personalised recommendation engines with user profiling

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Session 13: Natural Language Processing (NLP)

Students unlock the secrets behind Google Translate and chatbots by learning how AI processes human language. They built a word frequency analyzer that can identify the most important topics in any text. Through tokenization and text analysis, students gain an understanding of how computers interpret and analyze human communication.

Session 14: Image Processing in AI

This session reveals how AI "sees" the world by converting images into numerical data. Students use OpenCV to detect edges in photographs, gaining an understanding of how computers identify objects, faces, and patterns. They learn the fundamentals behind Instagram filters, medical imaging, and autonomous vehicle vision systems.

Session 15: AI in Gaming

Students create an intelligent game, such as Chess, where the AI is an opponent that learns winning strategies and adapts to player behaviour. Through game logic design, they understand how AI makes strategic decisions and plans multiple moves. This session demonstrates AI applications in entertainment, strategy, and competitive analysis.

Session 16: Sentiment Analysis

Students build an AI system that can determine whether movie reviews, social media posts, or product feedback express positive or negative opinions. Using real Twitter data and review datasets, they create practical tools for brand monitoring and customer feedback analysis. This session shows how businesses use AI to understand public opinion.

Session 17: AI in Recommendation Systems

Unveiling the magic behind Netflix and Amazon suggestions, students create their book recommendation engine. They learn collaborative filtering and content-based recommendations, understanding how AI personalizes experiences for millions of users. Students discover how recommendation systems drive modern e-commerce and content platforms.

Session 18: Ethics & Future of AI

Students engage in critical discussions about AI's impact on society, jobs, and privacy. Through role-playing as AI policymakers, they explore bias in AI systems, fairness in automated decisions, and the responsibility of AI creators. This session prepares students to be ethical AI practitioners who consider the broader implications of their work.

Session 19: Capstone Project Ideation

Students brainstorm and select their final project based on personal interests and real-world problem-solving opportunities. Whether creating an AI support for customer service, developing a face detection system for security, building a quiz generator for education, or designing a spam detector for email safety, students choose projects that demonstrate their AI mastery while addressing genuine needs.

Session 20: Data Collection & Preprocessing

Students learn the crucial skill of gathering and preparing real-world data for their AI systems. They discover data cleaning techniques, handle missing information, and format datasets for optimal AI performance. This session teaches the often-overlooked but essential foundation of successful AI projects.

Session 21: Model Development

Applying everything learned throughout the course, students train their AI models using appropriate algorithms and techniques. They make critical decisions about model architecture, feature selection, and training parameters. This hands-on development phase transforms theoretical knowledge into practical AI implementation skills.

Session 22: Model Testing & Tuning

Students learn the iterative process of improving AI performance through systematic testing and parameter adjustment for their capstone project. They discover how professional AI developers optimize models, handle edge cases, and improve accuracy. This session teaches the persistence and analytical thinking required for real-world AI development.

Session 23: Capstone Project Presentation Preparation

Students prepare comprehensive presentations showcasing their AI project, including problem identification, solution design, technical implementation, and real-world applications. They learn to communicate complex technical concepts to diverse audiences, developing crucial skills for academic and professional success.

Session 24: Final Presentation & Certification

Students present their completed AI projects (basics, core, and capstone) to mentors, parents, and peers, demonstrating their transformation from AI beginners to confident creators. They answer questions about their technical choices, discuss project challenges, and receive feedback from AI professionals. This culminating experience validates their AI competency and prepares them for advanced learning opportunities.