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📂 books 📖

📄 0: Reading List 📄 A Time of Gifts (Patrick Leigh Fermor, 1977) 📄 Top 5 Albums

📂 data-structures-and-algorithms

📄 0: Preface

📂 leetcode-patterns

📄 Arrays and Hashing 📄 Two Pointers 📄 Stack 📄 Binary Search 📄 Sliding Window 📄 Trees

📄 1: Graphs and Representations 📄 2: Graph Search 📄 3: Minimum Spanning Trees 📄 4: Union-Find 📄 5: Hash Tables 📄 6: Balanced Binary Search Trees 📄 7: Priority Queues and Heaps 📄 8: Tries 📄 9: Divide and Conquer 📄 10: Greedy Algorithms 📄 11: Dynamic Programming 📄 12: Network Flows 📄 13: Linear Programming 📄 14: NP-Completeness 📄 15: Approximation Algorithms 📄 16: Randomized Algorithms

📂 department-of-computer-science

📂 1-pythonta

📄 1. PythonTA

📂 2-markus

📄 1. MarkUs: Testing 📄 2. MarkUs: Framework Maintenance 📄 3. MarkUs: Performance 📄 4. MarkUs: Model Architecture 📄 5. MarkUs: Scheduled Visibility (Backend) 📄 6. MarkUs: Scheduled Visibility (Frontend) 📄 7. MarkUs: Touch Annotations

📄 Eight Months in SDS 📄 Reflections

📂 essays

📄 Coordination Without Authority: Zhang Zuolin and the Fengtian Clique 📄 The Feeling You Can't Verify: Napoleon, Tukhachevsky, and Bonapartism 📄 Discontinuous Utility and the Jutland Paradox 📄 The Translator's Eye: Language Acquisition as Epistemology 📄 Каскад at Bagram: VIA Music and the Compression Ratio of Sincerity

📂 firefox 🦊

📄 Firefox Contributions 📄 I. Notification Permission Telemetry 📄 II. Telemetry Revocation Events 📄 III. SmartBlock Link Preservation 📄 IV. Clear Data Dialog Cleanup 📄 V. Privacy Metrics - Design & Data Foundation 📄 VI. Privacy Metrics - Implementation 📄 VII. Privacy Metrics - UI & Testing

📂 frontend

📄 0: Vanilla JS Crash Course 📄 1: React Quick Start 📄 2: React Practice Components 📄 3: React Intermediate 📄 4: React Advanced Concepts 📄 5: Intermediate Practice Components 📄 6: Interview Simulation Components

📂 linear-algebra

📄 0: Preface 📄 1: Re-imagining Matrices 📄 2: Solving Linear Systems 📄 3: Vectors in Euclidian Space 📄 4: Matrix Operations 📄 5: Matrix Transformations as Functions 📄 6: Subspaces 📄 7: Kernel and Image 📄 8: Orthogonality and Projections 📄 9: The Determinant 📄 10: Eigenvalues and Eigenvectors 📄 11: Diagonalization and Similarity 📄 12: Orthogonal Diagonalization and the Spectral Theorem 📄 13: Singular Value Decomposition 📄 14: Portfolio Optimization 📄 15: PCA in Finance 📄 16: Covariance Estimation and Regularization

📂 machine-learning

📄 0: Preface

📂 1: K-Nearest Neighbors

📄 1.1: K-Nearest Neighbors 📄 1.2: Lab - MNIST Digit Classification

📂 2: Decision Trees

📄 2.1: Decision Trees 📄 2.2: Lab - Heart Disease Prediction

📂 3: Linear Regression

📄 3.1: Linear Regression 📄 3.2: Gradient Descent 📄 3.3: Lab - Linear Regression with Gradient Descent 📄 3.4: Practice Problems

📂 4-linear-classification

📄 4: Feature Mapping, Regularization, and Linear Classification

📄 5: Neural Networks 📄 6: Training Neural Networks 📄 7: Clustering 📄 8: Overfitting & Regularization 📄 9: Model Evaluation

📂 movies

📄 0: Watchlist 📄 Andrei Rublev and the Problem of Creation Under Uncertainty 📄 In the Heat of the Sun: Memory as Authorship

📂 multivariable-calculus

📄 Preface 📄 1: Parametric Equations / Polar Coordinates 📄 2: Vectors 📄 3: Vector Functions and Space Curves 📄 4: Differential Calculus of Several Variables 📄 5: Integral Calculus of Several Variables 📄 6: Line Integrals 📄 7: Surface Integrals 📄 8: Vector Calculus Theorems

📂 software-design

📄 Preface 📄 Object-Oriented Programming in Python 📄 SOLID Principles 📄 Design Patterns

📂 triathlon-program

📄 Training Schedule 📄 Nutrition Schedule

📂 us-government

📂 1-foundations

📄 Democratic Ideals & Types of Democracy 📄 Federalists vs. Anti-Federalists 📄 The Constitutional Convention 📄 Separation of Powers & Checks and Balances 📄 Federalism

📂 2-branches

📄 Congress: Structure and Powers 📄 Congressional Behavior 📄 The Presidency 📄 The Judiciary 📄 The Bureaucracy

📂 3-civil-liberties

📄 The Bill of Rights and Selective Incorporation 📄 First Amendment: Religion and Speech 📄 Press, Assembly, and the Right to Bear Arms 📄 Due Process, Criminal Rights, and Privacy 📄 Civil Rights and Equal Protection

📂 4-political-ideologies

📄 Political Socialization 📄 Measuring Public Opinion 📄 American Political Ideologies 📄 Ideology and Economic Policy 📄 Ideology and Social Policy

📂 5-political-participation

📄 Voting Rights and Turnout 📄 Political Parties 📄 Interest Groups and Lobbying 📄 Elections: Presidential and Congressional 📄 Campaigns, Finance, and Media

📄 Course Overview

Changelog v4.5 | Analytics

← home / machine-learning / 7: Clustering

| | 2025.12.31

7: Clustering

Coming soon.

Topics

K-means algorithm
Initialization strategies (k-means++)
Convergence properties
Hierarchical clustering
Choosing number of clusters
Silhouette score

Previous6: Training Neural Networks

Next8: Overfitting & Regularization

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