Advanced AI & Mathematics Ages 17-18

Grade 12: Transformers, LLMs, frontier AI, safety, research methods, and MLOps

Name: Grade 12 — AI & Computer Science
Author: AI Computer Institute

Transformers, LLMs, frontier AI, safety, research methods, and MLOps — structured as a full academic year with 4 units and 250 chapters.

📚 250 Chapters 📦 4 Units ❓ 201 Quiz Questions 🎯 CBSE-Aligned

📋 Table of Contents

250 chapters · 4 units

🔬 Unit 1: Advanced Architectures (Recap + Extend) 63 chapters

1.How Neural Networks Learn 2.Attention Is All You Need: The Paper That Changed Everything 3.Transformer Architecture Deep Dive 4.Large Language Models: From GPT to Claude 5.Diffusion Models: How AI Creates Images 6.AI Alignment and Safety 7.Graph Neural Networks 8.Federated Learning: Privacy-Preserving AI 9.How to Read AI Research Papers 10.Building Production AI Systems 11.Transformers and Attention Mechanisms: The Foundation of Modern LLMs 12.Prompt Engineering: The Art and Science of AI Interaction 13.Foundation Models: The Paradigm Shift in AI 14.Responsible AI and AI Safety: Building Trustworthy Systems 15.Mixture of Experts: How Modern LLMs Scale 16.Multimodal AI: GPT-4V, CLIP, and Beyond 17.RLHF: How ChatGPT Was Trained 18.Constitutional AI and AI Alignment 19.Benchmarks and Leaderboards: Measuring AI Progress 20.Reproducing Research: From Paper to Code 21.MLOps: CI/CD for Machine Learning 22.Distributed Training: Multi-GPU and Multi-Node 23.AI Security: Adversarial Attacks and Defenses 24.India's AI Policy: National Strategy and Implementation 25.Building the Transformer Architecture from Mathematical First Principles 26.RLHF: Reinforcement Learning from Human Feedback 27.Constitutional AI: Principled AI Alignment 28.Scaling Laws in Deep Learning 29.Sparse Attention Mechanisms 30.Mixture of Experts at Scale 31.Prompt Engineering Mastery: Advanced Techniques for LLM Control 32.Retrieval-Augmented Generation: Building Knowledge-Enhanced AI Systems 33.AI Agents and Frameworks: Building Autonomous Decision-Making Systems 34.Vision Transformers: Applying Transformer Architecture to Computer Vision 35.State Space Models and Mamba: Linear-Time Sequential Processing 36.Multimodal Training: Unified Vision-Language Models and Cross-Modal Alignment 37.AI Interpretability: Understanding Model Decisions Through SHAP, LIME, and Mechanistic Analysis 38.Synthetic Data Generation: GANs, Diffusion Models, and LLM-Based Data Creation 39.Model Merging: Combining Capabilities from Multiple Models 40.Chain-of-Thought Reasoning: Enabling Complex Step-by-Step Problem Solving 41.AI for Scientific Discovery: AlphaFold, Climate Modeling, and Materials Science Applications 42.Open-Source AI Ecosystem: HuggingFace, Ollama, vLLM, and GGUF Quantization 43.Tokenization Deep Dive: BPE, SentencePiece, Multilingual Tokenizers, and Efficiency 44.Inference Optimization: KV Cache, Speculative Decoding, and Batching Strategies 45.AI Evaluation Metrics: MMLU, HumanEval, HELM, Benchmarks, and Red-Teaming 46.Large Language Model Fine-tuning and LoRA 47.Parameter-Efficient Tuning and Model Compression 48.Dataset Curation and Data Quality 49.Data Decontamination and Test Set Leakage 50.AI and Copyright Law — Training Data, Outputs, and Indian IP 51.Compute Governance and AI Safety 52.AI Chip Design and GPU/TPU Architectures 53.Neuromorphic Computing and Spiking Neural Networks 54.Quantum Machine Learning and Quantum Computing 55.World Models and Imagination-Based Learning 56.Embodied AI and Embodied Learning 57.Multi-Agent Systems with Large Language Models 58.Code Generation and AI Programming Assistants 59.AI Reasoning Benchmarks and Evaluation 60.Frontier Model Safety and Alignment 61.EU AI Act: Global Regulatory Framework for AI Systems 62.Compute Governance and Scaling Laws: Managing AI Resource Allocation 63.Mechanistic Interpretability: Understanding AI System Internals for Safety

🤖 Unit 2: LLMs & Transformers 63 chapters

64.Superalignment Strategies: Aligning Superintelligent Systems 65.AI Existential Risk Analysis: Evaluating Long-Term Scenarios 66.Deceptive Alignment and Mesa-Optimization: Hidden Goals in AI Systems 67.Agentic AI Evaluation Frameworks: Testing Autonomous Systems 68.AI Red Teaming Methodology: Finding System Vulnerabilities 69.Watermarking AI-Generated Content: Detection and Attribution 70.AI and Biosecurity: Governance, Policy, and Institutional Oversight 71.AI in Geopolitics: Power Dynamics and Strategic Competition 72.Post-Training Enhancement: RLHF and Beyond 73.Multimodal Foundation Models: Architecture and Capabilities 74.AI Economics: Labor Market Disruption and Economic Transformation 75.Long-Context Reasoning and Retrieval: Processing Extended Information 76.Cloud Architecture Patterns: Building Scalable Systems 77.DevOps and CI/CD Pipelines: Automating Software Delivery 78.Brain-Computer Interfaces: Merging Brain and Machine 79.Vision Transformers: From ViT to DINOv2 80.Efficient Transformers: Linear Attention and Flash Attention 81.Mixture of Experts: Sparse Gating Networks 82.Neural Architecture Search: AutoML at Scale 83.Knowledge Distillation: Making Models Smaller 84.Model Quantization: INT8, INT4, and Binary Neural Networks 85.Diffusion Models: The Mathematics of Image Generation 86.State Space Models: Mamba and Beyond 87.Multimodal AI: Vision-Language Models 88.Neural Radiance Fields (NeRF): 3D from 2D 89.The Transformer Architecture: Attention is All You Need 90.Tokenization: BPE, WordPiece, and SentencePiece 91.Pre-training at Scale: Data, Compute, and Scaling Laws 92.Fine-tuning and Instruction Tuning 93.RLHF: Reinforcement Learning from Human Feedback 94.Prompt Engineering: From Zero-Shot to Chain-of-Thought 95.RAG: Retrieval-Augmented Generation 96.Constitutional AI and AI Safety 97.Agent Architectures: ReAct, Tree-of-Thought, Tool Use 98.Distributed Training: FSDP, DeepSpeed, and Pipeline Parallelism 99.AI Alignment: The Control Problem 100.Interpretability: Mechanistic Understanding of Neural Networks 101.Federated Learning: Privacy-Preserving AI 102.Differential Privacy: Mathematical Guarantees 103.Graph Neural Networks: Learning on Non-Euclidean Data 104.Neuromorphic Computing: Brain-Inspired Chips 105.Quantum Machine Learning: Quantum Advantage for AI 106.Causal Inference: Beyond Correlation 107.AI Governance: India's AI Policy Framework 108.The Compute Arms Race: GPU Economics and Sovereign AI 109.Reading Research Papers: A Systematic Approach 110.Experiment Design: Ablation Studies and Baselines 111.MLOps: From Notebook to Production 112.Model Serving: TorchServe, Triton, and vLLM 113.Building with APIs: Claude, GPT, and Gemini 114.Vector Databases: Embeddings at Scale 115.AI Startups: Building an AI Company in India 116.The AI Job Market: Career Paths for IIT Graduates 117.Open Source AI: Hugging Face, LangChain, and the Ecosystem 118.Capstone: Building a Production RAG System 119.LLM Pre-training at Scale: From Theory to Trillion Tokens 120.Tokenizer Design: BPE and SentencePiece Explained 121.Training Data Curation: The Art of Feeding Models Well 122.Data Decontamination: Ensuring Fair Model Evaluation 123.Distributed Training Fundamentals: Multi-GPU Essentials 124.Gradient Descent and Modern Optimizers 125.Normalization Techniques: Batch Norm and Layer Norm 126.Modern Activation Functions: ReLU, GELU, and Beyond

🛡️ Unit 3: Frontier AI 63 chapters

127.Weight Initialization Strategies: From Xavier to Kaiming 128.Learning Rate Scheduling and Warmup Strategies 129.Tensor Parallelism: Splitting Operations Across GPUs 130.Pipeline Parallelism: Minimizing Bubble Overhead 131.DeepSpeed ZeRO: Extreme Memory Efficiency 132.Mixed Precision Training: Float16 and Beyond 133.Flash Attention and Memory-Efficient Attention Variants 134.KV Cache Optimization and Management 135.Speculative Decoding: Speeding Sequential Generation 136.Retrieval-Augmented Generation: Combining LLMs with Knowledge 137.Vector Databases: Building Semantic Search Infrastructure 138.Embedding Models: Learning Dense Representations 139.Semantic Search at Scale: From Theory to Production 140.Prompt Engineering: Techniques for Better Outputs 141.Chain-of-Thought Prompting: Unlocking Reasoning Ability 142.Few-Shot and In-Context Learning 143.Parameter-Efficient Fine-tuning: Adapters and LoRA 144.Attention Mechanisms: The Foundation of Transformers 145.Transformer Architecture: Layer Design and Stacking 146.Scaling Laws: Understanding Model and Data Relationships 147.Compositional Learning: Building Complex from Simple 148.Interpretability Methods: Understanding Model Internals 149.AI for Healthcare: Medical Imaging and Drug Discovery 150.AI for Climate: Weather Prediction and Carbon Tracking 151.AI for Agriculture: Crop Prediction and Pest Detection 152.AI for Education: Adaptive Learning and Tutoring Systems 153.AI for Legal Applications: NLP and Contract Analysis 154.Constitutional AI: Aligning Models with Principles 155.RLHF: Complete Pipeline from Human Feedback to Alignment 156.Direct Preference Optimization: Learning from Preferences 157.Reward Model Training: From Human Judgments to Scoring 158.Red Teaming LLMs: Systematic Vulnerability Discovery 159.Jailbreak Detection and Defense Mechanisms 160.AI Safety Evaluation Frameworks: Measuring Safety Dimensions 161.The Alignment Tax: Trading Performance for Safety 162.Goodhart's Law in AI: When Metrics Stop Being Good Measures 163.Multimodal Models: Combining Vision and Language 164.Audio-Language Models: Speech and Text Integration 165.Video Generation Systems: From Concepts to Sora 166.Robotics Foundation Models: Learning Control Policies 167.Embodied AI: Grounding Intelligence in Robotics 168.Sim-to-Real Transfer: From Simulation to Physical Robots 169.Autonomous Driving Stack: End-to-End Systems 170.Drone Navigation and Control Systems 171.Swarm Intelligence: Collective Behavior Systems 172.Evolutionary Algorithms: Population-Based Optimization 173.Genetic Programming: Evolving Computer Programs 174.Neuroevolutionary Approaches: Evolving Neural Networks 175.TPU and GPU Architecture: Deep-Dive into AI Accelerators 176.CUDA Programming Basics: GPU Computing Fundamentals 177.Model Serving with TensorRT: Deployment Optimization 178.Inference Optimization Techniques: Speed and Efficiency 179.Mesa-Optimization: When Inner Optimizers Emerge 180.Deceptive Alignment: The Worst-Case AI Scenario 181.AI Governance Frameworks: Regulating Advanced AI 182.EU AI Act: Europe's Comprehensive AI Regulation 183.India's AI Regulation Path: Building Frameworks for AI 184.Responsible AI Deployment: From Research to Production 185.Quantum Computing Basics: Qubits and Quantum Algorithms 186.Quantum Machine Learning: Quantum Advantages in AI 187.Neuromorphic Computing: Brain-Inspired Architectures 188.Analog AI Accelerators: Computing with Physics 189.Uncertainty Quantification in Neural Networks

🎓 Unit 4: Research & Production 61 chapters

190.Adversarial Robustness: Defending Against Attacks 191.Continual Learning: Learning Without Catastrophic Forgetting 192.Meta-Learning: Learning How to Learn 193.Causal Inference in Machine Learning 194.Federated Learning: Distributed Privacy-Preserving Training 195.Building Large Language Models from Scratch: Tokenization to Training 196.Quantum Computing for AI: The Future of Computation 197.Building the Transformer: The Architecture That Changed AI 198.Scaling Laws: The Mathematical Blueprint Behind GPT-4 199.RLHF: How ChatGPT Learned to Be Helpful 200.Constitutional AI: Making AI Systems Harmless and Honest 201.Sparse Attention: Making Transformers Efficient at Scale 202.Mixture of Experts: Scaling Models Efficiently 203.Redis: Speed Up Your Apps with Caching 204.Cryptography: The Science of Secrets 205.Docker & Containers: Ship Code Anywhere 206.Microservices: Building Scalable Systems 207.Adversarial Robustness and Model Security 208.Bayesian Deep Learning: Uncertainty Quantification 209.Distributed Training: Scaling Deep Learning Across GPUs 210.DPO: Direct Preference Optimization 211.Flash Attention Optimization 212.Capsule Networks: Beyond Convolutions 213.Diffusion Models Simplified: From Noise to Art 214.Dynamic Programming: Value Iteration and Policy Iteration 215.Multi-Agent RL: Cooperative and Competitive 216.Reward Shaping and Inverse RL 217.Wasserstein GAN: Stable Training 218.DDPM: Denoising Diffusion Probabilistic Models 219.Distributed Consensus: Agreement in Faulty Systems 220.Federated Learning: Distributed Privacy-Preserving Training 221.Image Inpainting: Filling Missing Regions 222.Distributed Systems: Consensus & Replication 223.Knowledge Graph Completion: Link Prediction 224.PPO: Proximal Policy Optimization 225.Super-Resolution: Enhancing Image Quality 226.Temporal Graphs: Dynamic Graph Evolution 227.Thompson Sampling: Probabilistic Exploration 228.Video Understanding: Temporal Modeling 229.Mixture of Experts: Conditional Computation 230.Neural ODEs: Continuous Depth 231.Grouped Query Attention (GQA): Efficient Multi-Head Attention 232.Instruction Tuning: Making Models Follow Directives 233.Mamba: State Space Models for Sequence Processing 234.Protein Folding and Biological Sequence Modeling 235.Ring Attention: Distributed Attention Across Devices 236.Speculative Decoding: Faster Inference Through Speculation 237.Speech Recognition and Audio Processing 238.AI Career Portfolio: Building a Standout Application for College and Industry 239.CI/CD: Automated Testing and Deployment 240.Kubernetes Basics: Container Orchestration at Scale 241.Authentication: Sessions vs JWT Tokens 242.GraphQL Subscriptions: Real-time Data Streaming 243.WebAssembly: Running Native Code in Browsers 244.Logging and Monitoring: System Health Visibility 245.Rate Limiting: Protecting APIs from Abuse 246.Redis Caching: Speed Up Your Applications 247.RabbitMQ Message Queues: Async Communication 248.OAuth 2.0: Secure Authentication and Authorization 249.Message Queues: Asynchronous Processing with RabbitMQ 250.Serverless Functions: Event-Driven Computing

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250Serverless Functions: Event-Driven Computing

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