Unsupervised learning using neural networks is clearly machine learning since it is utilising neural nets.
However, some algorithms, k-means clustering, for example, are considered unsupervised learning, while they look just regular algorithms (non-ML).
What should be the borderline (criteria) to differentiate between unsupervised learning and a non-ML algorithm?
Any algorithm that uses data (in some form) to improve some performance measure (aka objective function), or to find some function, can be considered a machine learning algorithm. See this answer for more complete definitions of ML.
k-means does that. It uses the data to find some division of the data itself into groups, in order to maximize some objective function. So, k-means is a machine learning algorithm.
The use of neural networks is not a requirement for something to be called a machine learning approach. In fact, there are many other machine learning approaches/algorithms that do not use them, such as tabular Q-learning, support vector machines or hidden Markov models.
