- Machine learning, Andrew Ng - https://class.coursera.org/ml-004/class/index
Machine Learning Applications
- database mining (web click data, medical records, etc)
- applications such as autonomous helicopter, handwriting recognition, natural language processing (images and text)
- self-customising programs (product recommendations)
- understanding human learning
What is Machine Learning?
- Arthur Samuel (Samuel Checkers-playing Program) defines it as the field of study that gives computers the ability to learn without being explicitly programmed
- Tom M. Mitchell (author of the textbook Machine Learning) defined it as "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E" - example, if your email watches you label your emails as spam or not spam (E) and classifies subsequent emails as spam or not spam (T) which is measurable from the number of emails correctly classified as spam/notspam…
- Machine learning algorithms: supervised learning / unsupervised learning. also others: reinforcement learning, recommender systems.
- regression problem - getting the application to produce the right answers or values - (many number)
- classification problem - classifying something as 0 or 1 (sometimes there can also be more values) - discrete value output (0 or 1)
- support vector machine - support vector machines or support vector networks are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis