Artificial Intelligence
AI Foundations → Production Systems
Machine Learning•Neural Networks•Real-World Applications
"AI shapes the future. Master it from foundations to production, understanding both the power and responsibility of intelligent systems."
Programme Overview
The Artificial Intelligence curriculum is a comprehensive eight-level programme designed to transform learners from curious beginners into AI engineers. Starting with AI basics and chatbots at age 8, students progress through machine learning, deep neural networks, natural language processing, and computer vision, culminating in production-ready AI systems by age 15-16.
At every stage, students work with real datasets, build actual AI models, and explore the ethical implications of artificial intelligence in society.
We produce AI-literate problem-solvers who can design, build, and deploy intelligent systems responsibly.
Core Progression
- Foundations (Levels 1–2)Understanding AI basics through interactive experiments and simple chatbots.
- Core ML (Levels 3–4)Building and training machine learning models with real datasets.
- Intermediate AI (Levels 5–6)Building complete AI solutions with ethics and responsibility at the core.
- Advanced & Capstone (Levels 7–8)Mastering production-grade AI systems, deployment, and ethical considerations.
AI at a Glance
| Lvl | Age | Primary Focus |
|---|---|---|
| 1 | 8-9 | AI Foundations & Pattern Recognition |
| 2 | 9-10 | ML Fundamentals & Chatbots |
| 3 | 10-11 | Deep Learning & Computer Vision |
| 4 | 11-12 | Introduction to Machine Learning |
| 5 | 12-13 | Neural Networks & Deep Learning |
| 6 | 13-14 | NLP & Computer Vision Integration |
| 7 | 14-15 | Generative Models & LLMs |
| 8 | 15-16 | Advanced AI Systems & Deployment |
Detailed Master Plans
LEVEL 1 - AI Foundations & Chatbots
AI Foundations & Chatbots
Ages 8-9
Duration / Frequency: 36 Weeks | 2 Sessions/Week | 40 Min/Session
Aim: To introduce artificial intelligence concepts through interactive experiences, understanding what AI is, how it thinks, and building simple chatbots.
| Unit | Unit / Topic | Key Skills / Outcomes |
|---|---|---|
| 1 | What is Artificial Intelligence? | Understand AI basics and real-world examples |
| 2 | How Machines Learn | Introduction to machine learning concepts |
| 3 | Pattern Recognition | Identify patterns in data and images |
| 4 | Chatbot Basics | Create rule-based conversational agents |
| 5 | Decision Trees | Build simple decision-making systems |
| 6 | AI Ethics & Bias | Understand fairness and responsible AI |
| 7 | Image Recognition Basics | Explore computer vision concepts |
| 8 | Simple AI Projects | Build interactive AI applications |
LEVEL 2 - Machine Learning Fundamentals
Machine Learning Fundamentals
Ages 9-10
Duration / Frequency: 36 Weeks | 2 Sessions/Week | 45 Min/Session
Aim: To develop understanding of machine learning workflows, supervised and unsupervised learning, and practical applications using Python.
| Unit | Unit / Topic | Key Skills / Outcomes |
|---|---|---|
| 1 | Python for AI | Python basics for data science and AI |
| 2 | Data Collection & Preparation | Gather and clean data for ML models |
| 3 | Supervised Learning | Classification and regression basics |
| 4 | Unsupervised Learning | Clustering and pattern discovery |
| 5 | Training & Testing Data | Split data, validate models |
| 6 | Model Evaluation | Accuracy, precision, recall metrics |
| 7 | Introduction to TensorFlow | Use ML libraries and frameworks |
| 8 | ML Mini-Projects | Build predictive models |
LEVEL 3 - Deep Learning & Neural Networks
Deep Learning & Neural Networks
Ages 10-11
Duration / Frequency: 36 Weeks | 2 Sessions/Week | 50 Min/Session
Aim: To understand neural networks, deep learning architectures, and apply them to real-world problems like image and text recognition.
| Unit | Unit / Topic | Key Skills / Outcomes |
|---|---|---|
| 1 | Neural Networks Basics | Understand neurons, layers, activation functions |
| 2 | Forward & Backward Propagation | How neural networks learn and adjust weights |
| 3 | Convolutional Neural Networks | Image processing and computer vision |
| 4 | Recurrent Neural Networks | Processing sequential data and text |
| 5 | Optimization Techniques | Gradient descent and advanced optimizers |
| 6 | Transfer Learning | Reuse pre-trained models |
| 7 | Building Models with Keras | Create sophisticated neural network architectures |
| 8 | Deep Learning Projects | Image classification and text analysis |
LEVEL 4 - Natural Language Processing (NLP)
Natural Language Processing (NLP)
Ages 11-12
Duration / Frequency: 36 Weeks | 2 Sessions/Week | 50 Min/Session
Aim: To master natural language processing techniques, text analysis, and build intelligent systems that understand and generate human language.
| Unit | Unit / Topic | Key Skills / Outcomes |
|---|---|---|
| 1 | Text Processing Fundamentals | Tokenization, stemming, lemmatization |
| 2 | Word Embeddings | Word2Vec, GloVe, and semantic representation |
| 3 | Sentiment Analysis | Classify emotions and opinions in text |
| 4 | Named Entity Recognition | Extract names, places, and entities |
| 5 | Language Models | Transformers and attention mechanisms |
| 6 | Machine Translation | Building translation systems |
| 7 | Advanced NLP with BERT/GPT | Use state-of-the-art models |
| 8 | NLP Applications | Build chatbots and text analysis tools |
LEVEL 5 - Computer Vision Mastery
Computer Vision Mastery
Ages 12-13
Duration / Frequency: 36 Weeks | 2 Sessions/Week | 55 Min/Session
Aim: To develop advanced computer vision skills, understanding image processing, object detection, facial recognition, and real-world applications.
| Unit | Unit / Topic | Key Skills / Outcomes |
|---|---|---|
| 1 | Image Fundamentals | Pixels, channels, color spaces |
| 2 | Image Filtering & Enhancement | Edge detection, blurring, sharpening |
| 3 | Object Detection Models | YOLO, R-CNN architectures |
| 4 | Facial Recognition | Face detection and verification systems |
| 5 | Pose & Gesture Recognition | Understand body movements and actions |
| 6 | Segmentation Techniques | Semantic and instance segmentation |
| 7 | 3D Vision | Depth estimation and 3D reconstruction |
| 8 | Real-World CV Projects | Build autonomous vision systems |
LEVEL 6 - Reinforcement Learning & Autonomous Systems
Reinforcement Learning & Autonomous Systems
Ages 13-14
Duration / Frequency: 36 Weeks | 2 Sessions/Week | 55 Min/Session
Aim: To understand reinforcement learning, create intelligent agents that learn through interaction, and develop autonomous decision-making systems.
| Unit | Unit / Topic | Key Skills / Outcomes |
|---|---|---|
| 1 | Reinforcement Learning Basics | Agents, environments, rewards, policies |
| 2 | Q-Learning & DQN | Value-based learning algorithms |
| 3 | Policy Gradient Methods | REINFORCE, Actor-Critic algorithms |
| 4 | Game Playing AI | Build agents for games and simulations |
| 5 | Robotics & Control | Autonomous robot learning |
| 6 | Multi-Agent Systems | Agents that learn and interact together |
| 7 | Real-World Applications | Autonomous vehicles, game bots |
| 8 | RL Capstone Projects | Build complete autonomous systems |
LEVEL 7 - Generative AI & LLMs
Generative AI & LLMs
Ages 14-15
Duration / Frequency: 36 Weeks | 2 Sessions/Week | 60 Min/Session
Aim: To master generative models including GANs, diffusion models, and large language models, understanding how AI creates text, images, and media.
| Unit | Unit / Topic | Key Skills / Outcomes |
|---|---|---|
| 1 | Generative Adversarial Networks | How GANs generate synthetic data |
| 2 | Variational Autoencoders | Learning latent representations |
| 3 | Diffusion Models | Image generation and text-to-image |
| 4 | Large Language Models | Fine-tuning GPT and BERT models |
| 5 | Prompt Engineering | Crafting effective AI prompts |
| 6 | Text Generation | Story writing and code generation with AI |
| 7 | Multimodal AI | Vision and language combined models |
| 8 | Generative AI Applications | Create with AI tools and APIs |
LEVEL 8 - AI Engineering & Production Systems
AI Engineering & Production Systems
Ages 15-16
Duration / Frequency: 36 Weeks | 2 Sessions/Week | 60 Min/Session
Aim: To develop production-ready AI systems, understand deployment, scaling, monitoring, and ethical considerations for real-world AI applications.
| Unit | Unit / Topic | Key Skills / Outcomes |
|---|---|---|
| 1 | Model Deployment | Deploy models with Docker and cloud platforms |
| 2 | MLOps Fundamentals | CI/CD for machine learning |
| 3 | Model Monitoring & Maintenance | Track performance and drift in production |
| 4 | Scalability & Performance | Optimize models for speed and memory |
| 5 | AI Security & Privacy | Adversarial robustness and data privacy |
| 6 | Ethical AI & Responsible AI | Fairness, transparency, accountability |
| 7 | AI Research & Papers | Read and implement cutting-edge research |
| 8 | Capstone AI Project | Build, deploy, and present an AI solution |
Master Artificial Intelligence
From foundational concepts to production systems, develop expertise in machine learning, neural networks, and AI engineering that shapes tomorrow's technology.