Machine Learning Workshop

Individuals from all areas of infectious disease and infection science interested in learning more about data science and machine learning. No prior background in data science and machine learning is assumed and the workshop is suitable for beginners. 

1. Participants will develop an understanding of the “end-to-end” lifecycle of a machine learning solution.
2. Participants will develop a fundamental understanding of machine learning and data science.
3. Participants will learn how to develop their own machine learning workflow and models with no-code tools.
4. Everyone will receive copies of all the workshop material, in addition to supplementary material including more machine learning examples so participants can further explore the field and develop their understanding beyond the workshop.

The hands-on sections will involve participants using their own laptops, with the orange data mining software, which is free and open source and can be downloaded from https://orangedatamining.com/. More details will be provided to participants 8 weeks, 4 weeks, and 1 week before the workshop. 

Yes, a more detailed curriculum is provided as follows:

• Participants will be Introduced to data science and machine learning. Examples of machine learning applied for infection science will be discussed and we will provide sufficient background to support the hands-on components of the workshop.
• We will then proceed to discuss the "end-to-end" machine learning lifecycle. We will explore the key principles of building machine learning solutions, the types of machine learning problems, and when to use machine learning and when not to use it. We will discuss preparing and managing data for machine learning, training machine learning models, evaluating machine learning models, and briefly discuss other areas such as deployment, monitoring, and continual learning.
• Hands on session 1 will introduce participants to the software that we will be using (Orange data mining software), which is open source and free to download and use.
• Hands on session 2 will be about cleaning and preparing data for machine learning, and we will also discuss feature engineering.
• This will flow into hands on session 3 where we will start training different machine learning models and start implementing strategies for improving machine learning model performance.
• Hands on session 4 will focus on evaluation of our machine learning models, discussing how we can effectively evaluate machine learning performance, and the importance of correct machine learning model evaluation.
• Hands on session 5 will examine the important considerations for implementing machine learning in practice, and we will test our models with new data and retrain the models to try and improve performance.
• Towards the end of the workshop, I will provide detail about how to report machine learning experiments, with the goal of improving the quality of research being submitted for publication.

There will be time for questions, open discussion, technical support, and networking.