PCA and K means in the context of machine learning
It is always quite tricky to define Intelligence and Learning since they are very abstract concepts.
in my opinion, both algorithms are machine learning, and I will try to argue why.
Learning is (to me and Wikipedia) the action of improving at performing a task without being programmed explicitly. In our case, both algorithm improve at doing their task the more data we feed into them. And we do not code them explicitly, but instead we give them a goal : minimizing a loss function.
This is quite similar to the way humans learn. When we learn how to read, we get faster and faster and better at imagining what is written the more we read. Yet we do not know exactly how to read faster, we just know the objective function : minimize the time required to understand. And so our brain does the learning from its experiences (which are similar to the datas we feed to ML algorithms).
Another hint that leads to learning to me is the fact that both these algorithms won't give the same results twice if we initialize with random values. To compare to humans again, each person have his way of improving at the task : we all get better, yet each person has its own tricks for the task.
Anyway this is just my opinion on the subject and I do not pretend it does make sense to everyone.
Here is another interesting thread about PCA on Stack if you wanna read some more.
Hope my answer helps you in your philosophical reflection about this complicated topic.