Definition of ML given by Arthur Samuel in 1959
“ the field of study that gives computer the ability to learn without being explicitly programmed”.
Definition of ML given by Tom Mitchell in 1997
“ 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
In general, any ML problem can be assigned to one of 3 broad classifications :
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning : In the supervised learning, we are given a dataset and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
- Supervised Learning problems are categorized into “Regression” and “Classification” problems.
- In regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.
- In classification problem, we are instead trying to predict results in a discrete outputs. In other words, we are trying to map input variables into discrete categories.
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.
We can derive this structure by clustering the data based on relationship among the variables in the data.
Reinforcement Learning : In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.
Although the designer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills.