Curriculum/ai ml add 5 frontmatter fields

curriculum/03-AI-ML/01-beginner/01-foundations/01-math-for-ml.md

@@ -4,6 +4,14 @@ title: Math for Machine Learning
 track: ai-ml
 level: beginner
 version: 1.1
+Prerequisites: "Basic Python familiarity (NumPy introduced in this lesson)"
+Duration: "~45-60 minutes (including exercises)"
+Format: "Reading + hands-on NumPy exercises + end-to-end toy example"
+Future_Topics: "Data Pipelines Basics, Python Ecosystem, Model Lifecycle, Regression and Classification, Transformers"
+Related_docs:
+  - 01-foundations/01-concepts/01-learning-hierarchy
+  - 01-beginner/01-foundations/02-data-pipelines-basics
+  - 03-advanced/01-modern-architectures/01-transformers
 ---
 
 # Math for Machine Learning

curriculum/03-AI-ML/01-beginner/01-foundations/02-data-pipelines-basics.md

@@ -4,6 +4,14 @@ title: Data Pipelines Basics
 track: ai-ml
 level: beginner
 version: 1.1
+Prerequisites: "01-math-for-ml, basic pandas"
+Duration: "~50-70 minutes"
+Format: "Watch First video + reading + pandas examples + six-stage pipeline exercise"
+Future_Topics: "Model Lifecycle, MLOps, Monitoring and Drift, Feature Engineering, Deployment Patterns"
+Related_docs:
+  - 01-beginner/01-foundations/01-math-for-ml
+  - 01-beginner/01-foundations/03-model-lifecycle
+  - 02-intermediate/02-mlops/02-monitoring-and-drift
 ---
 
 # Data Pipelines Basics

curriculum/03-AI-ML/01-beginner/01-foundations/03-model-lifecycle.md

@@ -4,6 +4,14 @@ title: Model Lifecycle
 track: ai-ml
 level: beginner
 version: 1.1
+Prerequisites: "01-math-for-ml, 02-data-pipelines-basics"
+Duration: "~45-60 minutes"
+Format: "Watch First video + reading + 7-stage lifecycle sketch exercises"
+Future_Topics: "Python Ecosystem, Notebooks and Visualization, ML Libraries, Supervised Learning, MLOps, CI/CD for Models"
+Related_docs:
+  - 01-beginner/01-foundations/02-data-pipelines-basics
+  - 01-beginner/02-tools/01-python-ecosystem
+  - 02-intermediate/02-mlops/01-ci-cd-for-models
 ---
 
 # Model Lifecycle

curriculum/03-AI-ML/01-beginner/02-tools/01-python-ecosystem.md

@@ -4,6 +4,14 @@ title: The Python Ecosystem
 track: ai-ml
 level: beginner
 version: 1.1
+Prerequisites: "Basic Python and CLI familiarity; 03-model-lifecycle recommended"
+Duration: "~45-60 minutes"
+Format: "Reading + hands-on setup (venv, pip install, first scikit-learn model)"
+Future_Topics: "Notebooks and Visualization, ML Libraries, Supervised Learning, Feature Engineering"
+Related_docs:
+  - 01-beginner/01-foundations/03-model-lifecycle
+  - 01-beginner/02-tools/02-notebooks-and-visualization
+  - 01-beginner/02-tools/03-ml-libraries
 ---
 
 # The Python Ecosystem

curriculum/03-AI-ML/01-beginner/02-tools/02-notebooks-and-visualization.md

@@ -4,6 +4,14 @@ title: Notebooks and Visualization
 track: ai-ml
 level: beginner
 version: 1.1
+Prerequisites: "01-python-ecosystem"
+Duration: "~45-60 minutes"
+Format: "Watch First video + reading + interactive Jupyter exercises (histograms, scatter, missing-value visualization)"
+Future_Topics: "ML Libraries, Supervised Learning, Model Lifecycle (production handoff)"
+Related_docs:
+  - 01-beginner/02-tools/01-python-ecosystem
+  - 01-beginner/02-tools/03-ml-libraries
+  - 06-rust-engineering/13-testing-fixtures-code-review-culture
 ---
 
 # Notebooks and Visualization

curriculum/03-AI-ML/01-beginner/02-tools/03-ml-libraries.md

@@ -4,6 +4,14 @@ title: ML Libraries
 track: ai-ml
 level: beginner
 version: 1.1
+Prerequisites: "01-python-ecosystem, 02-notebooks-and-visualization"
+Duration: "~45-60 minutes"
+Format: "Reading + runnable scikit-learn example (LinearRegression, MAE/MSE, train_test_split) + selection table"
+Future_Topics: "Supervised Learning (Regression and Classification), Feature Engineering, Hyperparameter Tuning"
+Related_docs:
+  - 01-beginner/02-tools/02-notebooks-and-visualization
+  - 02-intermediate/01-supervised-learning/01-regression-classification
+  - 02-intermediate/01-supervised-learning/02-feature-engineering
 ---
 
 # ML Libraries

curriculum/03-AI-ML/02-intermediate/01-supervised-learning/01-regression-classification.md

@@ -4,6 +4,14 @@ title: Regression and Classification
 track: ai-ml
 level: intermediate
 version: 1.1
+Prerequisites: "03-ml-libraries; basic linear algebra and probability helpful (covered in 01-math-for-ml)"
+Duration: "~60-75 minutes"
+Format: "Watch First video + reading + scikit-learn baseline + threshold-tuning exercise"
+Future_Topics: "Feature Engineering, Hyperparameter Tuning, MLOps Evaluation, Monitoring and Drift"
+Related_docs:
+  - 01-beginner/02-tools/03-ml-libraries
+  - 02-intermediate/01-supervised-learning/02-feature-engineering
+  - 02-intermediate/02-mlops/02-monitoring-and-drift
 ---
 
 # Regression and Classification

curriculum/03-AI-ML/02-intermediate/01-supervised-learning/02-feature-engineering.md

@@ -4,6 +4,14 @@ title: Feature Engineering
 track: ai-ml
 level: intermediate
 version: 1.1
+Prerequisites: "01-regression-classification, pandas fluency"
+Duration: "~60-75 minutes"
+Format: "Watch First video + reading + scikit-learn ColumnTransformer + Pipeline exercise + leakage-check mental model"
+Future_Topics: "Hyperparameter Tuning, CI/CD for Models, Drift Monitoring, Deployment Patterns"
+Related_docs:
+  - 02-intermediate/01-supervised-learning/01-regression-classification
+  - 02-intermediate/01-supervised-learning/03-hyperparameter-tuning
+  - 02-intermediate/02-mlops/01-ci-cd-for-models
 ---
 
 # Feature Engineering

curriculum/03-AI-ML/02-intermediate/01-supervised-learning/03-hyperparameter-tuning.md

@@ -4,6 +4,14 @@ title: Hyperparameter Tuning
 track: ai-ml
 level: intermediate
 version: 1.1
+Prerequisites: "01-regression-classification, 02-feature-engineering"
+Duration: "~60 minutes"
+Format: "Watch First video + reading + GridSearchCV/RandomizedSearchCV exercises + tuning-budget exercise"
+Future_Topics: "CI/CD for Models, Monitoring and Drift, Deployment Patterns, Advanced Architectures"
+Related_docs:
+  - 02-intermediate/01-supervised-learning/02-feature-engineering
+  - 02-intermediate/02-mlops/01-ci-cd-for-models
+  - 03-advanced/01-modern-architectures/01-transformers
 ---
 
 # Hyperparameter Tuning

curriculum/03-AI-ML/02-intermediate/02-mlops/01-ci-cd-for-models.md

@@ -4,6 +4,14 @@ title: CI/CD for Models
 track: ai-ml
 level: intermediate
 version: 1.1
+Prerequisites: "03-model-lifecycle, 02-feature-engineering, 03-hyperparameter-tuning, basic Git/GitHub Actions"
+Duration: "~60-75 minutes"
+Format: "Watch First video + reading + local CI script + GitHub Actions YAML exercise"
+Future_Topics: "Monitoring and Drift, Deployment Patterns, Model Cards, Model Registry Tooling (MLflow)"
+Related_docs:
+  - 02-intermediate/02-mlops/02-monitoring-and-drift
+  - 02-intermediate/02-mlops/03-deployment-patterns
+  - 01-foundations/03-tooling/02-version-control
 ---
 
 # CI/CD for Models

curriculum/03-AI-ML/02-intermediate/02-mlops/02-monitoring-and-drift.md

@@ -4,6 +4,14 @@ title: Monitoring and Drift
 track: ai-ml
 level: intermediate
 version: 1.1
+Prerequisites: "01-ci-cd-for-models, 01-regression-classification metrics"
+Duration: "~60-75 minutes"
+Format: "Watch First video + reading + PSI from-scratch + daily prediction aggregation + triage/rollback/retrain playbook"
+Future_Topics: "Deployment Patterns, Advanced Monitoring Tooling (Evidently, Prometheus), Retraining Triggers, Model Retirement"
+Related_docs:
+  - 02-intermediate/02-mlops/01-ci-cd-for-models
+  - 02-intermediate/02-mlops/03-deployment-patterns
+  - 03-advanced/02-research-in-practice/03-ethics-and-responsibility
 ---
 
 # Monitoring and Drift

curriculum/03-AI-ML/02-intermediate/02-mlops/03-deployment-patterns.md

@@ -4,6 +4,14 @@ title: Deployment Patterns
 track: ai-ml
 level: intermediate
 version: 1.1
+Prerequisites: "01-ci-cd-for-models, 02-monitoring-and-drift; basic FastAPI/Docker helpful"
+Duration: "~60-75 minutes"
+Format: "Watch First video + reading + code-along (joblib batch, FastAPI /predict, streaming diagram, rollout plan)"
+Future_Topics: "Modern Architectures (Transformers, GNN, RL, CNN), Large Model Alignment, Ethics and Responsibility"
+Related_docs:
+  - 02-intermediate/02-mlops/01-ci-cd-for-models
+  - 03-advanced/01-modern-architectures/01-transformers
+  - 06-rust-engineering/15-observability-performance-deployment
 ---
 
 # Deployment Patterns

curriculum/03-AI-ML/03-advanced/01-modern-architectures/01-transformers.md

@@ -4,6 +4,14 @@ title: Transformers
 track: ai-ml
 level: advanced
 version: 1.1
+Prerequisites: "01-math-for-ml, deep learning basics, PyTorch helpful"
+Duration: "~75-90 minutes"
+Format: "Watch First video + reading + NumPy attention from scratch + Hugging Face DistilBERT example"
+Future_Topics: "Graph Neural Networks, Reinforcement Learning, CNN, Large Model Alignment, 07-agent-systems Memory and State"
+Related_docs:
+  - 01-beginner/01-foundations/01-math-for-ml
+  - 03-advanced/01-modern-architectures/02-graph-neural-networks
+  - 07-agent-systems/04-memory-and-state
 ---
 
 # Transformers

curriculum/03-AI-ML/03-advanced/01-modern-architectures/02-graph-neural-networks.md

@@ -4,6 +4,14 @@ title: Graph Neural Networks
 track: ai-ml
 level: advanced
 version: 1.1
+Prerequisites: "01-transformers (helpful), linear algebra, PyTorch helpful"
+Duration: "~75-90 minutes"
+Format: "Watch First video + reading + NumPy mean-aggregator on a learning-path graph + GCN/GAT/GraphSAGE comparison"
+Future_Topics: "Reinforcement Learning, CNN, Knowledge Graphs, PyTorch Geometric"
+Related_docs:
+  - 03-advanced/01-modern-architectures/01-transformers
+  - 04-Protocol Engineering/01-beginner/01-protocol-concepts/02-state-machines
+  - 07-agent-systems/05-planning-and-reasoning
 ---
 
 # Graph Neural Networks

curriculum/03-AI-ML/03-advanced/01-modern-architectures/03-reinforcement-learning.md

@@ -4,6 +4,14 @@ title: Reinforcement Learning
 track: ai-ml
 level: advanced
 version: 1.1
+Prerequisites: "01-math-for-ml, probability; 01-transformers / 02-graph-neural-networks familiarity"
+Duration: "~75-90 minutes"
+Format: "Watch First video + reading + Q-learning from scratch in NumPy + reward-audit exercise + safety checklist"
+Future_Topics: "Paper Replication, Large Model Alignment (RLHF/DPO), Ethics and Responsibility, Offline RL, Gymnasium"
+Related_docs:
+  - 01-beginner/01-foundations/01-math-for-ml
+  - 03-advanced/02-research-in-practice/02-large-model-alignment
+  - 07-agent-systems/06-safety-and-guardrails
 ---
 
 # Reinforcement Learning

curriculum/03-AI-ML/03-advanced/01-modern-architectures/04-cnn.md

@@ -4,6 +4,14 @@ title: Convolutional Neural Networks
 track: ai-ml
 level: advanced
 version: 1.0
+Prerequisites: "01-math-for-ml, linear algebra, PyTorch basics"
+Duration: "~60-75 minutes"
+Format: "Watch First video (bare iframe) + reading + PyTorch mini ConvNetwork exercise (Conv2d + AdaptiveAvgPool2d + Linear)"
+Future_Topics: "3D Computer Vision, Video Understanding, Scene Reconstruction, Advanced Vision Transformers"
+Related_docs:
+  - 01-beginner/01-foundations/01-math-for-ml
+  - 03-advanced/01-modern-architectures/01-transformers
+  - 06-rust-engineering/16-beyond-backend-cli-data-protocols-wasm-embedded
 ---
 
 # WHAT IS CNN?  (CONVOLUTIONAL NEURAL NETWORKS)

curriculum/03-AI-ML/03-advanced/02-research-in-practice/01-paper-replication.md

@@ -4,6 +4,14 @@ title: Paper Replication
 track: ai-ml
 level: advanced
 version: 1.1
+Prerequisites: "03-ml-libraries, 01-transformers, PyTorch, Git basics"
+Duration: "~60-90 minutes (with optional multi-day replication project)"
+Format: "Watch First video + reading + replication spec + mini-replication exercise + replication-report template"
+Future_Topics: "Large Model Alignment, Ethics and Responsibility, Ablations, ML Reproducibility Challenge Submissions"
+Related_docs:
+  - 01-beginner/02-tools/03-ml-libraries
+  - 03-advanced/01-modern-architectures/01-transformers
+  - 01-foundations/03-tooling/02-version-control
 ---
 
 # Paper Replication

curriculum/03-AI-ML/03-advanced/02-research-in-practice/02-large-model-alignment.md

@@ -4,6 +4,14 @@ title: Large Model Alignment
 track: ai-ml
 level: advanced
 version: 1.1
+Prerequisites: "01-paper-replication, 01-transformers, 03-reinforcement-learning"
+Duration: "~75-90 minutes"
+Format: "Watch First video + reading + preference-pair design + rubric + red-team exercise + tiny DPO loss function"
+Future_Topics: "Ethics and Responsibility, Reward Hacking, Scalable Oversight, Evaluation Harnesses, 07-agent-systems Safety and Guardrails"
+Related_docs:
+  - 03-advanced/02-research-in-practice/01-paper-replication
+  - 03-advanced/01-modern-architectures/03-reinforcement-learning
+  - 07-agent-systems/06-safety-and-guardrails
 ---
 
 # Large Model Alignment

curriculum/03-AI-ML/03-advanced/02-research-in-practice/03-ethics-and-responsibility.md

@@ -4,6 +4,15 @@ title: Ethics and Responsibility
 track: ai-ml
 level: advanced
 version: 1.1
+Prerequisites: "02-large-model-alignment, 02-monitoring-and-drift, 01-ci-cd-for-models"
+Duration: "~60-75 minutes"
+Format: "Watch First video + reading + demographic-parity / equal-opportunity checks in pandas + model card + stakeholder map"
+Future_Topics: "NIST AI RMF Application, Regulatory Compliance, Model Cards in Registries, Ongoing Audit Programs"
+Related_docs:
+  - 03-advanced/02-research-in-practice/02-large-model-alignment
+  - 02-intermediate/02-mlops/02-monitoring-and-drift
+  - 02-intermediate/02-mlops/01-ci-cd-for-models
+  - 07-agent-systems/06-safety-and-guardrails
 ---
 
 # Ethics and Responsibility

website/docusaurus.config.ts

@@ -50,7 +50,7 @@ const config: Config = {
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           routeBasePath: "curriculum",
           sidebarPath: "./sidebars.ts",
-          editUrl: "https://github.com/Flow-Research/learn/tree/main/",
+          editUrl:({ docPath }) => `https://github.com/Flow-Research/learn/edit/main/curriculum/${docPath}`,
           remarkPlugins: [remarkMath],
           rehypePlugins: [rehypeKatex],
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