lectures/Machine-Learning_MOC.md

metadata

title: Machine-Learning Map of Content (MOC)
course: '[[Machine-Learning]]'
type: moc
updated: '2026-08-20'
tags:
  - moc
  - course/Machine-Learning

🗺️ Machine-Learning: Map of Content (MOC)

Master Course Knowledge Hub This hub connects all lecture notes, derivations, and exam warnings for Machine-Learning.

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📅 1. Chronological Lecture Syllabus

Date	Topic	Note Link
2026-08-19	Backpropagation	[[2026-08-19_Machine-Learning_Backpropagation|Backpropagation Note]]

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📐 2. Key Derivations & Theorems Index

No formal theorems indexed yet.

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⚠️ 3. High-Yield Exam Pitfalls Aggregator

From [[2026-08-19_Machine-Learning_Backpropagation|Backpropagation (2026-08-19)]]:

• Dimension Analysis: Always perform a dimensional consistency check. Ensure the resulting matrix $\frac{\partial L}{\partial W^{(l)}}$ matches the dimensions of the original weight matrix $W^{(l)}$.
• Saturated Activations: Be prepared to explain the "Vanishing Gradient" phenomenon. Specifically, identify that when $\sigma'(Z) \to 0$ (e.g., in the tails of a Sigmoid function), the gradient signal effectively vanishes, preventing effective learning in deeper layers.
• Caching: Do not forget that the backward pass is strictly dependent on the activations stored during the forward pass; this is a common "trick" question regarding memory complexity.