The Learning Problems
- When Can Machines Learn? (illustrative + technical)
- Why Can Machines Learn? (theoretical + illustrative)
- How Can Machines Learn? (technical + practical)
- How Can Machines Learn Better? (practical + theoretical)
When Can Machines Learn?
Some definitions
Learning: acquiring skill with experience accumulated from observations
Machine learning: acquiring skill with experience accumulated/computed from data
data -----> ML ------> sklill
Skill: improve some performance measure (e.g. prediction accuracy)
data -----> ML ------> improved performance measure
The Machine Learning Route
ML: an alternative route to build complicated systems
Give a computer a fish, you feed it for a day; teach it how to fish, you feed it for a lifetime
Key Essence of Machine Learning
Improving some performance measure with experience computed from data
- exists some underlying pattern to be learned
- but no programmable (easy) definition
- somehow there is data about the pattern
Formalize the Learning Problem
inputs: \(x \in \chi\)
outputs:\(y\in \gamma\)
unknown pattern to be learned <=> target function
\[f: \chi \rightarrow \gamma\]
data <=> training examples \[D = \{(x_1,y_1),(x_2,y_2),......(x_N,y_N)\}\]
hypothesis <=> skill with hopefully good performance
\[g: \chi \rightarrow \gamma\]
Learning Flow
Learning Model
Practical Definition of Machine Learning
Machine Learning: use data to compute hypothesis g that approximates target f
Relationships between Machine Learnig, Data Mining, Artifical Intelligence and Statics
- Machine Learning
use data to compute hypothesis g that approximates target f. ML can realize AI, among other routes(\(g \approx f\) is something that shows intelligent behavior)
- Data Mining
use (huge) data to find property that is interesting.
- Artifical Intelligence
compute something that shows intelligent behavior.
- Statics
use data to make inference about an unknown process, statistics is a useful tool for ML.