docs: Add comprehensive curriculum plans for future ML repositories

Add detailed planning documents for two companion repositories:

- UNSUPERVISED_ML_PLAN.md: Complete curriculum for unsupervised learning
  including clustering (K-Means, DBSCAN, GMM), dimensionality reduction
  (PCA, t-SNE, UMAP), anomaly detection, matrix factorization, topic
  modeling, and deep unsupervised learning (autoencoders, VAE).
  12 lessons + 4 X-series guides = 32 notebooks planned.

- REINFORCEMENT_LEARNING_PLAN.md: Complete curriculum for RL from MDPs
  to modern deep RL, covering classical methods (DP, MC, TD learning),
  deep RL (DQN, PPO, SAC), advanced topics (multi-agent, hierarchical,
  offline RL). 15 lessons + 4 X-series guides = 38 notebooks planned.

Both follow the same pedagogical approach: theory + practical notebooks,
from first principles, story-driven, Google Colab compatible.

Author: claude