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.

Machine learning

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

  • Linear Regression
  • Logistic Regression (Classification)
  • Neural Networks
  • Support Vector Machine

• ­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:

1. 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.
2. All machine learning algorithms are based on mathematics, and thus expect all data to be numbers.
3. Usually more data is better, but not if the data is redundant. That is, duplicate examples or features.

More to come in future posts.