classes of ml

what is machine learning?

: designing algorithms for inferring what is unknowns from knowns.





applications :

– spam detection

– handwriting detection

– in google streetview : blurring out people’s faces (face detection) / license plates

– speech recognition

– netflix : preference, recommendation

– navigation for robot : integrating all sensor, video data, etc

– climate modelling : weather pattern prediction




_Classes of ML problems


supervised learning

: canonical

given (x1, y1) … (xn, yn)

xi : data points

yi : target value

Find a function f(x) = y

– classification : yi belong to finite set

– regression : yi in real value





classiification :

이 때 x는 벡터일 수도 있다.  xi = (xi_1, xi_2)



이때 새로운 xn이 주어졌을 때 yn은 0일 것인지 1일 것인지 판단하는 것.


regression :



새로운 any given x에 대해서 y의 값을 예측하는 function을 찾아내는 것.




unsupervised learning


given (x1, … , xn) (각 x는 여러 dimension이 있을 수 있음.)



find patterns in the data.

– clustering

– density estimation

– dimenstionality reduction




: 이렇게 데이터를 어떤 특성에 따라 나누는 cluster를 찾아내는 것.


_density estimation

assuming these data xi come from some probability distribution,

and u want to estimate the density.






find some lower dimensional space that u can represent this data lies on.



each point will be projected on to lower dimensional dignosis




* while preserving the structure of the data

(단순한 projection으로는 structure 정보가 날라간다.)







_ semi-supervised learning



(x1, y1) … (xk, yk), xk+1, ….. xn


predict the labels for the remaning points

(some of values are missing (ex. Netflix problem : 중간중간 빈 데이터들이 있다.))


혹은, 다음과 같은 문제.





active learning

: ask for information (labels)



decision theory

: measuring performance (how many u got wrong)







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