Description
Today, Machine Learning and AI technologies are transforming science, but knowing how to get started can feel overwhelming. Our workshop introduces foundational concepts and methods of Machine Learning with practical examples and exercises.
Join CBW’s intensive 3-day hands-on workshop to learn, apply and assess machine learning techniques. Learn from our experts, network with peers and start your Machine Learning journey!
• Where: Halifax, Nova Scotia, Canada
• When: October 14-16, 2026
The Canadian Bioinformatics Workshops (CBW) offered through bioinformatics.ca have been teaching Bioinformatics for over 25 years, reaching over 4000 students through more than 200 workshops.
You will gain experience in:
• Applications and Limitations of Machine Learning and Deep Learning;
• Decision Trees and Random Forests;
• Artificial Neural Networks (ANNs);
• Fundamentals of clustering as an unsupervised machine learning method;
• Machine learning operations principles; and
• Applying these machine learning techniques for biomarker discovery, secondary structure prediction and more.
Only 30 seats are available, so reserve your seat today!
Prerequisites
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.
Details
Learning Objectives
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.
Who Should Enrol
Graduates, postgraduates, Principal Investigators and Professionals working with or about to embark on using machine learning for bioinformatics applications.