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ComputingLifeSci

A complete computational biology curriculum — 23 courses covering programming, databases, machine learning, genomics and research infrastructure. Written for biologists, not computer scientists.

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Programming for Biology

Python for Biologists
R for Biologists
Perl for Biologists
MATLAB for Biologists
SAS for Biologists
BioJava for Biologists

Data & Databases

SQL for Biological Databases
Bioinformatics Databases
Big Data Mining for Biologists

AI & Machine Learning

Machine Learning for Biologists
Artificial Intelligence for Biologists
MLOps for Biologists
AI Tools for the Bioinformatics Lab

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Machine Learning for Biologists82 chapters

Chapter 6.1 — K-Means and Hierarchical Clustering
Chapter 6.2 — DBSCAN and Gaussian Mixture Models
Chapter 6.3 — Independent Component Analysis (ICA) and Association Rule Mining

/Machine Learning for Biologists/PART 6 — Unsupervised Learning

Chapter 26 of 828 min read

Chapter 6.1 — K-Means and Hierarchical Clustering

1. Learning Objectives Understand clustering as grouping samples by similarity with no labels Understand how K Means and Hierarchical Clustering work step by step Choose an appropriate number of clusters and interpret…

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PreviousChapter 5.7 — Neural Networks (Foundations Before Deep Learning)NextChapter 6.2 — DBSCAN and Gaussian Mixture Models