Introduction to Stats and Machine Learning

30 Oct 2024

🚧 Work in progress…

This chapter covers fundamental statistical concepts and machine learning techniques that form the mathematical foundation of modern AI systems.

What you’ll learn in this chapter:

Statistical inference and machine learning provide the theoretical underpinnings for understanding and building AI systems. This chapter explores key concepts from probability, statistics, and classical machine learning that are essential for working with modern AI.

Statistical Foundations

• Biased and Unbiased Estimators: Understanding estimation theory and statistical properties
• The Curse of Dimensionality: Challenges and implications of high-dimensional data

Probabilistic Models

• Variational Inference: Approximate Bayesian inference for complex probabilistic models
• Dirichlet Process and Stick Breaking: Non-parametric Bayesian methods for clustering
• Chinese Restaurant Process: Elegant probabilistic models for partition structures

Matrix Factorization and Decomposition

• Non-negative Matrix Factorization (NMF): Lee-Seung algorithm and applications in dimensionality reduction

These foundational concepts are crucial for understanding how modern AI systems learn from data, handle uncertainty, and make predictions. While deep learning has transformed the field, these statistical principles remain relevant for interpreting model behavior, designing experiments, and developing new algorithms.

By the end of this chapter, you’ll have a solid understanding of the mathematical tools that enable machine learning and can apply them to real-world problems.