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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

Overview
Chapter 3.1 — Arithmetic, Algebra, Functions, Graphs, and Logarithms
Chapter 3.2 — Vectors, Matrices, Eigenvalues, and Eigenvectors
Chapter 3.3 — Calculus: Derivatives, Gradients, and Optimization
Chapter 3.4 — Probability, Random Variables, Bayes' Theorem, and Distributions
Chapter 3.5 — Statistics: Hypothesis Testing, Correlation, Covariance, Variance, and Sampling

/Machine Learning for Biologists/PART 3 — Mathematics for Machine Learning

Chapter 8 of 821 min read

Overview

(Taught from absolute zero — no prior math background assumed)

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PreviousChapter 2.2 — Types of Machine Learning NextChapter 3.1 — Arithmetic, Algebra, Functions, Graphs, and Logarithms