a program that can teach itself and improve over time.
can be used in:
finds line of best fit. continus data
we have lots of data, each dot is a new image. it’s scattered in space, doesnt matter.
clustering - clustering data together dimensionatly reduction - line of best fit but not a line, a curvey boi
we can build a vector of all features
nominal: no ordering among possible values (boolean) ordinal: possible values of the feature are totally ordered
training instances are independent and identically (I.I.D.) - sampled independently from the same unknown distribution
there are also cases where this assumption does not hold
the primary purpose in supervised learning is to find a model that generalises
todo: read up on these: ecision trees • neural networks • support vector machines • Bayesian networks • ensembles of the above
odor = a: e (400)
400 is confidence
clustering - we want to make sure that we cluster into groups with lots of similarity but don’t connect them into groups that don’t have similarity.
what is a decision tree?
should i just submit my facebook chatbot, which uses k neartest neighbor?
key hyptohesis: the simplest tree that classifies the training instances accurately will work well on previously unseen instances.
entities should not be multipled beyond necessity When you have two competing theories that make exactly the same predictions, the simpler one is the better.