COMS W4771 Lecture Notes - Lecture 1: Spacex, Binary Classification, Machine Translation
24 May 2017
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Document Summary
Intelligence = learning = prediction: statistician: breiman, industry learning, very efficient, machine learning tacks, 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, 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. Find what the groups are and the similar features. Below a certain threshold: machine learning applications. Interdisciplinary (cs, math, stats, physics, or, psych: data-driven approach to ai, many domains are too hard to do manually, for example (any type of large data sets): Speech recognition: computer vision, genomics, nlp and parsing, medical, behavior/games. Text and inforetrieval: machine learning is a subset of artificial intelligence, using statistical approach based on data. Example 1: image classification: goal: automatically recognize bird species in new photos.
