Categories: Artificial Intelligence
Unsupervised learning is a type of AI-based algorithm that relies on characteristics instead of labeled datasets that are used in supervised learning. A typical application is the ability to classify or cluster datasets based on their characteristics. For example, an unsupervised learning algorithm can easily classify fruit based on color, size, and shape. The algorithm does not know what a banana is, but it will create a common group for anything resembling a banana. In projects, three uses of unsupervised learning are for risks, task complexity, and change requests.
1) Risks. Using unsupervised learning to cluster risks might result in finding a common cause for a group of risks or developing a shared mitigation strategy. Clustering risks from several projects can also result in finding a risk on your project that was overlooked.
2) Task Complexity. For this application, tasks are grouped by complexity based on the task definitions. If there is an unusually high number of complex tasks, the project manager needs to evaluate the ability of assigned resources to complete them. Additional training or mentoring from an expert may be required. A review of the resource allocation plan may alleviate any concerns. A high level of complex tasks can also provide an incentive to validate the risk management plan.
3) Change requests. Grouping change requests from previous similar projects can result in being able to forecast expected changes on your project. This proactive approach allows more accurate estimates for budget and schedule. If all changes in your project require additional funding and a shift in the end date, that is good. However, the sheer number of changes or unexpected changes may still result in a deterioration of project performance.
In project management, unsupervised learning finds patterns that a project manager cannot detect. Finding these patterns allows proactive actions to be taken that keep the project on a trajectory for success.
