Summary and Schedule
A workshop teaching concepts and skills required for researchers to develop and validate artificial intelligence models.
Target Audience
Our primary audience is graduate students/early career researchers who have or are going to have data and want to begin applying ML/DL/AI methods to extract insights. We also hope to help: Research group leaders; educators; others who want to expand their understanding of the technologies so they can better advise other people.
Learning Objectives
By the end of the workshop, learners will be able to…
- Define common terms encountered in artificial intelligence, including deep learning, machine learning, and large language models.
- Summarise the difference between supervised and unsupervised methods, and the kinds of tasks these different methods are suited to.
- Discuss how experimental design and choices made when data is collected can influence the quality and evaluation of a machine learning model.
- Prepare data for use in a machine learning application, through normalisation, labeling, and other pre-processing steps.
- Train machine learning and deep learning models for regression and classification tasks.
- Compare some popular metrics to evaluate the quality of a model and apply these.
- Identify common issues with a model including bias and overfitting.
Lessons
Note: the curriculum for this workshop is in early but active development. We recommend the Introduction to Deep Learning lesson in The Carpentries Lab if you want to start learning similar skills right away.
| Lesson | Overview |
|---|---|
| Connecting Key Concepts in Machine Learning | Build understanding of key concepts in machine learning and artificial intelligence, describe relationships between these concepts, and connect them to the research context. |
| Preparing Data for Machine Learning | Organise, clean, label, and format data so that it is ready to be used in the training and validation of a model. |
| Developing and Evaluating a Deep Learning Model | Use supervised learning to train random forest and neural network models that can predict the similarity or categories of input data. Apply methods to validate the models you develop and estimate the quality of their predictions. |
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Using Markdown | How do you write a lesson using Markdown and sandpaper? |
| Duration: 00h 12m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
FIXME: Setup instructions live in this document. Please specify the tools and the data sets the Learner needs to have installed.
Data Sets
Download the data zip file and unzip it to your Desktop
Software Setup
Details
Setup for different systems can be presented in dropdown menus via a
spoiler tag. They will join to this discussion block, so
you can give a general overview of the software used in this lesson here
and fill out the individual operating systems (and potentially add more,
e.g. online setup) in the solutions blocks.
Use PuTTY
Use Terminal.app
Use Terminal