Machine Learning(Arthur Samuel, 1959): Field of study that gives computersthe ability to learn without being explicitly programmed.
Well-posed Learning Problem(Tom Mitchell, 1998): A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P,improves with experience E.
You are given a datasets with inputs X and lables Y and your goal is to learn a mapping from X to Y.
the term regression refers to that the value Y you’re trying to predict is continuous.
the term classification refers to the Y takes on a discrete number of variables. (the output is discrete)
(the top symbol is a different way of visualizing the same data)
classfication - muliple features
more features in reality problem
X: road state / Driver direction
Y: steering direction
X: road state / Arbitrator Direction
Y1: steering direction in one -lane road
Y2：steering direction in two-lane road
Learn more systematic engineering principles for machine learning to efficiently figure out what to do next.
It’s one subset of machine learning that’s really hot right now.
CS330 more narrowly covers just deep learning.
You given a dataset with no labels, just given inputs X and no Y and your goal is find something interesting(eg. figure out the structure in this data) about that.
some unsupervised learning problem example:
news clusters / genetic microarray data / organize computing clusters / social network cohesive communities analysis / market segmentation / astronomical data analysis: clustering problem
cocktail party problem: a ICA(Independent Componenets Analysis) problem
learning analogies: man is to woman as king is to queen, Tokyo is to Japan as Washington DC is to the United States. You can learning this relation just from texts on the Internet.
Giving signals called a reward signal, you can have a learning algorithm figure out by itself how to optimize the reward.
For example: you can’t teach a dog the optimal way to behave(you aren’t a dog), but you can go “Oh, good dog” whenever it behavs well, go “bad dog” when it misbeaves. Over time, the dog learns to do more of the good dog things and fewer of the bad dog things.