I recently finished the machine learning class offered online by Stanford. It was a great experience. Since I would not be using ML any time soon, I plan to make a few blog posts to capture my learning while they are still current. This should help me recollect the concepts on a later date. If someone finds these notes useful, that is an added benefit.
Arthur Samuel (1959): Field of study that gives computers the ability to learn without being explicitly programmed.
Machine learning algorithms (covered in the class):
- Supervised learning
- Unsupervised learning
- k-Means clustering
- Principal Component Analysis
My understanding of machine learning:
Given a set of examples with certain features; the ability of a computer to approach, and surpass, the ability of a human expert at analysing and extracting meaning out of the given data.
Important points about ML:
- If a human expert does not find the data sufficient to come up with a conclusion, then the computer is unlikely to perform any better.
- All machine learning algorithms are based on mathematics, and thus expect all data to be numbers.
- Usually more data is better, but not if the data is redundant. That is, duplicate examples or features.
More to come in future posts.