Machine Learning Algorithms
- Supervised Learning: teach the computer how to do something
- Unsupervised Learning: learn by itself
- Reinforcement Learning
- Recommender Systems
Tom Mitchell provides a more modern definition: "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: playing checkers.
- E = the experience of playing many games of checkers
- T = the task of playing checkers.
- P = the probability that the program will win the next game.
Supervised Learning
In supervised learning,
we are given a data set and already know what our correct output should look like.
- regression means that our goal is to predict a continuous valued output.
- classification is to predict a discrete value output.
Unsupervised Learning
Unsupervised learning allows us to approach problems with little or no idea what our results should look like.
We can derive structure from data where we don’t necessarily know the effect of the variables.