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// Stage 2 · Classical Machine Learning

Unsupervised Learning

k-means, Gaussian mixtures and EM, PCA, t-SNE/UMAP.

What you will learn ↓ Code & exercises ↓

about 8 hours6 algorithms2 paperslesson text in progress

01Goals

Do these first: The ML Workflow: Data, Loss & Generalization · Linear Algebra

02Algorithms to master

Each card has the original paper, prerequisites and resources.

k-Means Clustering Alternates assigning points and recomputing centers to form k clusters. 1982core Expectation–Maximization (EM) Alternates inferring hidden variables and updating parameters. 1977ext Principal Component Analysis (PCA) Reduces dimensions by keeping directions of greatest variance. 1901core t-SNE Maps high-dimensional data to 2D while keeping neighbors close. 2008ext UMAP A faster dimensionality reduction that better preserves global structure. 2018ext DBSCAN (Density-Based Clustering) Clusters by local density; finds arbitrarily shaped clusters and flags noise. 1996ext

03Must-read papers

04Courses & videos

05Hands-on: project, code & exercises

Free, official or author-published pages. Most run in the browser or on a free Colab GPU.

PROJECT

Cluster the embeddings of 1,000 sentences with k-means, plot them in 2-D with UMAP and name each cluster.

  1. 1
    scikit-learn.org ↗Notebooks

    Compare k-means, DBSCAN and Gaussian-mixture clusterings on the same data.

  2. 2
    github.com ↗Notebooks

    Run the PCA and GMM notebooks and derive EM yourself.

06Self-test

Answer in your head, then open to check.

Is k-means sensitive to initialisation?

Yes — k-means++ and multiple restarts are common.

Can you read distances between clusters on a t-SNE plot?

Not directly.

What direction is PCA’s first principal component?

The direction of greatest variance in the data.

On these routes: Algorithm Researchers · Domain Scientists

Data updated: AI algorithms & curriculum(UTC+8)