> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Foundations

> Statistical learning theory fundamentals - the learning problem, regression, entropy, optimization, and classification.

These chapters cover the foundational concepts of statistical learning theory that underpin modern AI and machine learning systems.

## Topics

<CardGroup cols={2}>
  <Card title="The Learning Problem" icon="lightbulb" href="/aiml-common/lectures/learning-problem/index">
    Understanding the Vapnik framework and the learning problem formulation.
  </Card>

  <Card title="Linear Regression" icon="chart-line" href="/aiml-common/lectures/regression/linear-regression/index">
    Extracting non-linear patterns with linear models.
  </Card>

  <Card title="Entropy" icon="shuffle" href="/aiml-common/lectures/entropy/index">
    Information theory principles and their applications.
  </Card>

  <Card title="SGD & Optimization" icon="arrow-trend-down" href="/aiml-common/lectures/optimization/sgd/index">
    Stochastic gradient descent and optimization techniques.
  </Card>

  <Card title="Classification Intro" icon="tags" href="/aiml-common/lectures/classification/classification-intro/index">
    Introduction to classification problems.
  </Card>

  <Card title="The Neuron (Perceptron)" icon="brain-circuit" href="/aiml-common/lectures/classification/perceptron/index">
    The perceptron algorithm, the fundamental building block of neural networks.
  </Card>

  <Card title="Logistic Regression" icon="wave-square" href="/aiml-common/lectures/classification/logistic-regression/index">
    Binary classification with logistic regression.
  </Card>
</CardGroup>

***

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