Is pattern recognition the same as unsupervised learning? Is machine learning the same as supervised learning?
Nice topic. I think it depends on how the "learning" process by a machine is defined, so if we assume that a machine can "autonomously" learn from experience (i.e. new data as it becomes available every X time), why not to include also semi-supervised and unsupervised learning styles? 
The more data the machine has about the system being modeled, the more info to find more precise patterns which correctly describe the system, for all kind of learning styles.

As an example I made to explain how the one-class support vector machine works, we could see that, although an unsupervised learning process does not have a ground truth (i.e. labels) or a semi-supervised learning has only the "normality" label, the machine is able to better describe the system as it receives more data and as some hyperparameters are adjusted, if you test your model with known points wich should be outside the learnt cluster (where an anomaly score could be also a metric in this case).