1. data -> ML -> skill
skill: improve some performance measure
machine learning: improve some performance measure with experience computed from data
decide whether to use ML:
(1)exists some "underlying pattern" to be learned
(2)no programmable definition
(3)somehow there is data about the pattern
2. The learning model:
training examples, hypothesis set ---> learning algorithm ---> final hypothesis g
3. Difference:
Machine Learning: use data to compute hypothesis g that approximates target f
Data Mining: use huge data to find property that is intersting
Artificial Intelligence: compute something that shows intelligent behavior
Statistics: use data to make inference about an unknown process
4.
5. Perceptron Learning Algorithm (PLA)
-- A fault confessed is half redressed.
next can follow naive cycle(1..N)
6. Linear Separability: if PLA can halt(stop)
7. Pocket Algorithm: modify PLA algorithm by keeping best weights in pocket.
maker fewer mistakes until enough iterations
pocket is slower than PLA, because it needs to compare with old w and store better weight.
8. Multiclass classification problem: which type
regression: stock price, temperature
binary classification: y={-1, +1}
structured learning:
9. Supervised learning: every Xn comes with corresponding Yn
Unsupervised learning: multiclass classification <=> 'clustering', learning without Yn
eg: articles => topics
Semi-supervised learning: coin recognition with some Yn
Reinforcement learning: learn with "partial/implicit information" (often sequentially)
10. Different Input space: concrete, raw, abstract features
11. Hoeffding's Inequality
