Theoretical Foundations of Machine Learning

Welcome to the theoretical course on machine learning! This path dives deep into the mathematical principles, algorithms, and concepts that underpin modern ML systems. 🧠📚

Course Overview

This course covers:

• 📚 Mathematical Foundations: Linear algebra, calculus, probability, and statistics
• 🧠 Algorithm Theory: Supervised/unsupervised learning, convergence analysis
• 🔍 Statistical Learning Theory: Bias-variance tradeoff, generalization bounds
• 🧩 Optimization Methods: Gradient descent, convex optimization
• 🌐 ML Applications: Real-world use cases and theoretical challenges

Key Topics

• Bayes' Theorem

  Explore probabilistic reasoning and its role in ML models.

• VC Dimension

  Understand the theoretical limits of learning algorithms.

• No-Free-Lunch Theorem

  Learn why no single ML algorithm works best for all problems.

Recommended Extensions

For hands-on practice, check out our Hands-on_Practical_Guides path. 🚀
For a broader perspective, explore our ML_Overview resource. 📚

Resources

• Advanced_Statistical_Methods
• Optimization_Theory
• Deep_Learning_Theory

Let me know if you'd like to dive deeper into any specific topic! 🌟