Intro to Machine Learning in genome informatics
Graduate course, University of British Columbia, 2024
Introductory Machine Learning Lectures
These are two introductory lectures I delivered as a guest lecturer between 2024 and 2026 as part of BMEG 424 / Genome Informatics at UBC. Their goal is to familiarize students with core machine learning concepts that can support their projects in genome informatics. The lectures provide a broad introduction to the language, intuition, and practical foundations of modern machine learning, with an emphasis on ideas that students are likely to encounter in computational biology and genomics.
Lecture materials
| Lecture | Topic | Description | Materials |
|---|---|---|---|
| Lecture 1 | Introduction to Machine Learning | A broad introduction to the main ideas of machine learning, including learning tasks, model training, evaluation, and core terminology relevant to genome informatics. | |
| Lecture 2 | Neural Networks and Modern ML | An introductory overview of neural networks and modern machine learning approaches, with a focus on concepts that help students engage with current methods used in biological data analysis. |
Notes
These lectures are intended as a practical and conceptual entry point rather than a comprehensive treatment of machine learning. They are designed to help students build enough intuition to read papers, begin using standard tools, and think more critically about how machine learning methods apply to problems in genome informatics.