Information Theory for AI
Master the mathematical foundations that power modern machine learning. From Shannon's entropy to cross-entropy loss, understand the information-theoretic principles behind every neural network, language model, and classification system.
What You'll Learn
By the end of this course, you'll understand the information-theoretic foundations that underpin loss functions, model evaluation, and generative AI.
Entropy & Uncertainty
Understand Shannon entropy, how it measures uncertainty in probability distributions, and why it matters for AI models.
Divergence Measures
Learn KL divergence and how it quantifies the difference between probability distributions used in training and inference.
Cross-Entropy Loss
Master the most important loss function in deep learning — from classification to language modeling.
Mutual Information
Explore how mutual information measures dependencies between variables and its applications in feature selection and representation learning.
Course Lessons
Follow the lessons in order for a complete understanding, or jump to any topic.
1. Introduction
What is information theory? How Claude Shannon's 1948 paper laid the foundation for modern AI and why every ML engineer should understand it.
2. Entropy
Shannon entropy, conditional entropy, joint entropy, and the chain rule. Calculating uncertainty in probability distributions with Python examples.
3. KL Divergence
Kullback-Leibler divergence explained: measuring how one distribution differs from another. Applications in VAEs, policy optimization, and model distillation.
4. Mutual Information
How mutual information quantifies shared information between variables. Uses in feature selection, representation learning, and information bottleneck theory.
5. Cross-Entropy Loss
The workhorse loss function of deep learning. Binary and categorical cross-entropy, softmax, log-loss, and how language models are trained.
6. Best Practices
Practical guidelines for applying information theory in ML projects: numerical stability, choosing metrics, debugging loss curves, and common pitfalls.
Prerequisites
What you need before starting this course.
- Basic probability theory (distributions, expected value, conditional probability)
- Python fundamentals and familiarity with NumPy
- High school calculus (logarithms, summation notation)