1. when can machines learn?
1.1 The Learning Problem
1.1.1 what is machine learning
learning: acquiring skill
with experience accumulated from observations
observations --> learning --> skill
machine learning: acquiring skill <-> improving some performance measure
with experience accumulated/computed from data
data --> machine learning --> skill (improved performance measure)
ML: an alternative route to build complicated systems
Some Use Scenarios • when human cannot program the system manually —navigating on Mars • when human cannot ‘define the solution’ easily —speech/visual recognition • when needing rapid decisions that humans cannot do —high-frequency trading • when needing to be user-oriented in a massive scale —consumer-targeted marketing
Key Essence of Machine Learning
1 exists some ‘underlying pattern’ to be learned —so ‘performance measure’ can be improved 2 but no programmable (easy) definition —so ‘ML’ is needed 3 somehow there is data about the pattern —so ML has some ‘inputs’ to learn from
1.1.2 Components of Machine Learning A takes D and H to get g
1.1.3 Machine Learning and Other Fields
difficult to distinguish ML and DM in reality
Machine Learning: use data to compute hypothesis g that approximates target f Data Mining: use (huge) data to find property that is interesting
• if ‘interesting property’ same as ‘hypothesis that approximate target’ —ML = DM (usually what KDDCup does) • if ‘interesting property’ related to ‘hypothesis that approximate target’ —DM can help ML, and vice versa (often, but not always) • traditional DM also focuses on efficient computation in large database
ML is one possible route to realize AI
Machine Learning: use data to compute hypothesis g that approximates target f Artificial Intelligence: compute something that shows intelligent behavior
• g ≈ f is something that shows intelligent behavior —ML can realize AI, among other routes • e.g. chess playing • traditional AI: game tree • ML for AI: ‘learning from board data’
statistics: many useful tools for ML
Machine Learning: use data to compute hypothesis g that approximates target f Statistics: use data to make inference about an unknown process • g is an inference outcome; f is something unknown —statistics can be used to achieve ML • traditional statistics also focus on provable results with math assumptions, and care less about computation
