This workshop is intended to provide an introduction to machine learning and its application to bioinformatics. This workshop is not intended for machine learning experts. Instead, it targets biologists or other life scientists who want to understand what machine learning is, what it can do and how it can be used for a variety of bioinformatic or medical informatics applications.
Students will gain experience in:
- Applications and Limitations of Machine Learning and Deep Learning
- Decision Trees and Random Forests – how they work, how they are coded in Python, and how they can be used in bioinformatic applications (biomarker discovery and modeling)
- Applications and Limitations of Machine Learning and Deep Learning
- Decision Trees and Random Forests – how they work, how they are coded in Python, and how they can be used in bioinformatic applications (biomarker discovery and modeling)
- Artificial Neural Networks (ANNs) – how they work, how data is encoded, how they are coded in Python, and how they can be used in bioinformatic applications (classification and secondary structure prediction)
- Fundamentals of clustering as an unsupervised machine learning method
- Machine learning operations principles.
Graduates, postgraduates, Principal Investigators and Professionals working with or about to embark on using machine learning for bioinformatics applications.
Familiar with the basics of Python.
You will also require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 1.5GHz CPU, 2GB RAM, 10GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements). If you do not have access to your own computer, you may loan one from the CBW. Please contact support@bioinformatics.ca for more information.
This workshop requires participants to complete pre-workshop tasks and readings.
Module 1: Introduction to Machine Learning
- Instances of Machine Learning (ML) (supervised and unsupervised; classification and regression).
- What ML can and cannot do.
- ML in bioinformatics.
- Measuring the performance of ML algorithms.
- Overfitting/underfitting
Module 2: [Supervised Learning] Classification using tree-based algorithms
- Fundamentals of Decision Trees and Random Forest algorithms.
- Live coding implementation of Decision Trees and Random Forest.
- Hyperparameter tuning.
- Assessing model performance.
- Random Forest feature importance metric
Module 3: [Supervised Learning] Classification using Artificial Neural Networks
- Fundamentals of a multilayer perceptron Artificial Neural Network (ANN).
- Mathematical concepts on weight updating and backpropagation.
- Live coding an ANN in Python.
- Defining architecture.
- Hyperparameter tuning.
- Assessing model performance.
Module 4: [Supervised Learning] Model Interpretability (“opening the black box”)
- Fundamentals of the Shapley Additive Explanations (SHAP) framework.
- Adding a post-hoc interpretation layer to the Random Forest model (Module 2) and the Artificial Neural Network model (Module 3).
- Interpreting SHAP outcomes.
Module 5: [Supervised Learning] Regression using Artificial Neural Networks
- Fundamentals of a regression.
- Regularization techniques.
- Adapting a Neural Network to solve a regression problem.
- Assessing model performance.
Module 6: [Unsupervised Learning] Clustering
- Fundamentals of clustering.
- Centroid-based clustering (k-means) and hierarchical clustering.
- Live coding implementation of a k-means and hierarchical clustering.
- Methods for determining the optimal number of clusters (Elbow, Silhouette)
Module 7: Conclusions
- Machine Learning Operations (MLOps) principles.
- Model persistence and deployment.
- Workshop wrap-up
Duration: 3 days
Start: Oct 14, 2026
End: Oct 16, 2026
Status: Application Open
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Canadian Bioinformatics Workshops promotes open access. Past workshop content is available under a Creative Commons License.
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