Information Theory, From Coding to Learning: Why Polyanskiy & Wu’s Textbook Is the Bridge Modern Learners Need
If you’ve learned a bit about entropy, bits, and Shannon’s channel capacity, but still can’t see how it all connects to modern machine learning, you’re not alone. Most textbooks treat classical information theory and statistical learning as separate worlds. The result? Students can compress files in theory but struggle to explain how KL divergence tightens…