Firstly, here is the definition of a well-posed learning problem:
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
My machine learning professor claims that supervised learning is machine learning and unsupervised learning is pattern recognition. Here are his definitions of machine learning and pattern recognition:
Machine Learning is learning from experience. It’s also called supervised learning. E consists of features and labels, and P and T are well-defined.
Pattern Recognition is finding patterns without experience. It’s also called unsupervised learning. E consists of only features, and P and T are defined in much broader terms of finding ‘interesting patterns'.
If I cross reference this with other sources on the internet, I get different definitions. They say that machine learning involves both supervised and unsupervised learning. Likewise, pattern recognition involves both supervised and unsupervised learning. Some say that pattern recognition is machine learning. Some even say the opposite, that machine learning is a form of pattern recognition.
So is my professor right, or is the internet right, or is the answer somewhere in between? Can we answer this question definitively?