Introduction to Machine Learning  Machine Learning: the study of computational mechanisms that "learn" from data in order to make predictions and decisions o Statistical data-driven Computation Models o Real domains (vision,speech behavior):  No E=MC^2  Noisy, complex, nonlinear  Have many variables  Non-deterministic  Incomplete, approximate models o Need:statistical models driven by data & sensors o Bottom-up: use data to form a model o Why? Complex data everywhere, audio, video, internet o Intelligence = Learning = Prediction o Statistician: Breiman, industry learning, very efficient  Machine Learning Tacks o Supervised: algorithms where we have the answers in advanced and making forecasts for future data (a known relationship/function). The learning part happens where the results can be compared to with expected values.  Classification  ???? > 0  Regression  ???? = ???? o Unsupervised: algorithms don't know in advanced the labels/clusters/relevant features. Exploring what we see and figure out what information we have.  Modeling/Structuring  ???? ????  Representing data, help organize data  Clustering  Separating into common characteristics  Find what the groups are and the similar features  Feature Selection  Extracting most relevant features  Detection  ???? ???? < ????  Below a certain threshold  Machine Learning Applications o Interdisciplinary (CS, Math, Stats, Physics, OR, Psych) o Data-driven approach to AI o Many domains are too hard to do manually o For example (any type of large data sets):