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General ML Notes

General

These notes provide a comprehensive foundation in machine learning concepts, combining theoretical understanding with practical intuition. The material is drawn from StatQuest videos and Cornell's CS4780 course, two excellent resources for learning ML.

Purpose and Goals

These notes aim to help you:

  1. Understand the fundamentals: Build a solid foundation in statistics and probability before diving into ML algorithms
  2. Develop intuition: Learn not just the "how" but the "why" behind each technique
  3. See connections: Understand how different algorithms relate to each other
  4. Apply knowledge: Gain practical insights for implementing these methods

Topics Covered

  1. Basic Statistics: Sampling, probability, hypothesis testing, confidence intervals—the foundation for all ML
  2. Decision Trees: Intuitive tree-based models that recursively partition the feature space
  3. Gradient Boosting: The powerful technique of combining weak learners into a strong ensemble
  4. XGBoost: The industry-standard implementation of gradient boosting with key optimizations
  5. Clustering: Unsupervised techniques for discovering structure in unlabeled data
  6. Support Vector Machines: Maximum margin classifiers with the kernel trick for non-linear boundaries
  7. Dimensionality Reduction: Techniques like PCA and t-SNE for handling high-dimensional data
  8. Regression: From simple linear regression to logistic regression for classification

Prerequisites

Before starting, you should be comfortable with:

  • Basic algebra and calculus (derivatives, partial derivatives)
  • Linear algebra fundamentals (vectors, matrices, dot products)
  • Basic probability concepts (events, conditional probability)

How to Use These Notes

  • Start with Basic Statistics if you need to refresh foundational concepts
  • For each topic, focus on understanding the intuition before the math
  • Pay attention to the "why" questions—understanding motivation helps retention
  • Work through examples to solidify understanding

Resources

  • StatQuest with Josh Starmer: Excellent video explanations with clear visualizations
  • Cornell CS4780: More rigorous treatment of machine learning theory