lectures/Machine-Learning_MOC.md

---
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)

> [!NOTE] 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.