Machine learning for bio- and medical-informatics

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Many fields have massive amounts of data -- ranging from log files from web browsers, to telemetry data from equipment, to hospital records. The patterns within this data can be very valuable; eg these patterns may identify which machine needs preventative maintanence, which sites a user may want to visit and which products s/he is likely to purchase, or even identify which patients will respond well to a given treatment. Unfortunately, these patterns may be difficult to extract, due to both the quantity, and complexity, of the data. The field of machine learning (ML) provides many technologies, and tools, for this task. Much of this work involves understanding, developing and using tools that take as input a "labeled dataset" of instances (eg, a set of patients, coupled with their respective clinical outcomes), and returns a classifier, which can map novel instances to labels -- eg, to determine whether a patient, with a given microarray, will respond well to some treatment.