Compared to old-fashioned AI methods, Modern AI methods have the ability to deal with uncertainty because of which these methods work in the real world.
Ability to think of uncertainty as a thing that can be quantified at least in principle. Uncertainty is not beyond the scope of rational thinking and discussion, and probability provides a systematic way of doing just that.
Key Points :
- probability can be quantified (expressed as a number) and it can be right or wrong.
- probability can be used to automate uncertain reasoning
Used to weigh conflicting pieces of evidence in medicine, in a court of law, and in many scientific disciplines.
# the odds that it will rain later today(example)
chances of rain: 206 in 365
number of days without rain: 159
Prior odds for rain : 206:159
chances of clouds on a rainy day : 9 out of 10
chance of blue skies on a rainy day: 1 out of 10
chances of having clouds on a rainless day : 1 out of 10
how much higher are the chances of clouds on a rainyday compared to a rainless day ?
answer: chances of clouds are nine times higher on a rainy day than on a rainless day.posterior odds = likelihood ratio * prior odds = 9* (206/359) = 1854:159
The Bayes classifier is a machine learning technique that can be used to classify objects such as text documents into two or more classes. The classifier is trained by analyzing a set of training data, for which the correct classes are given.
- Naive Bayes classifier can be used to determine the probabilities of the classes given a number of different observations.
- Real world application: spam filters