Bioinformatics & Deep Learning — Course Notes

An open-access companion for the redesigned KB8029 course

Welcome

This is an open, interactive companion to the redesigned KB8029 Bioinformatics course. Rather than teaching classical bioinformatics and machine learning as two separate blocks, this book follows the course’s integrated session-by-session structure — see the Course Schedule for the full plan, or course-redesign.md in the repository root for the rationale behind it.

Offline or printed? All course pages are also available as a single PDF: KB8029-course-notes.pdf. The notebooks are not included; they are openly available on the website.

This is not a copy of any textbook. Earlier we used two copyrighted books (Magnus Ekman’s Learning Deep Learning for the machine-learning half and Jonathan Pevsner’s Bioinformatics and Functional Genomics for the classical half). Our lectures build on that earlier work, but nothing from either book is reproduced here. Every page is written from scratch, drawing on fully open sources — none restricted to non-commercial use — that cover the same material:

• Dive into Deep Learning (Zhang, Lipton, Li & Smola) — CC BY-SA 4.0. Primary source for every machine-learning session.
• Wikipedia — CC BY-SA 4.0. Conceptual and algorithmic depth for the bioinformatics sessions.
• EMBL-EBI Training — CC BY 4.0. The practical, tool-use layer for databases and alignment.
• bibook (Lukas Käll) — CC BY 4.0. Notebook code for the algorithmic bioinformatics sessions (alignment, MSA, and more).
• NCBI Bookshelf — public domain (US government work). BLAST and database practicalities.
• RCSB PDB-101 — CC BY 4.0 (educational tier only). Protein structure.
• A Byte of Python — CC BY-SA 4.0. Python fundamentals and object-oriented programming.

Every page names which source(s) it draws on, and because of the CC BY-SA license, pages adapted from Dive into Deep Learning or Wikipedia are themselves shared here under CC BY-SA 4.0.

Students are never pointed at raw source material directly — Wikipedia in particular is written to be browsed outward via its own links, which makes “how far should I read?” an unanswerable question. Every session gets one bounded, authored page instead, with sources listed for further reading, never as an inline dependency.

New to the course? Start with Session 0: Introduction to the Course: what the course covers, how a session works, and what to do before Session 1. The rules (exam, grading, labs, plagiarism, AI tools) and the contact details of the student office are on Course Information & Logistics.

How to use this alongside the course

Most session pages have three parts:

1. The idea — a short, from-scratch explanation of the session’s core concept, written to match what’s covered in the lecture.
2. The course notebook — the session’s material in code, embedded with its saved output so you can read it here or run it yourself in Colab. Sessions with a lab also have a separate lab notebook, linked from the page’s lab section; you answer its questions in the lab quiz on Canvas.
3. What’s next — an explicit pointer to where this session’s ideas resurface later in the course. The redesign is built around several continuous threads (most centrally, secondary-structure prediction) rather than disconnected topics, and each page names the connection rather than leaving it implicit.