This last week a nine-week online course entitled “Learning From Data”started, taught by by Caltech Professor Yaser Abu-Mostafa. As they promoted… “A real Caltech course, not a watered-down version, broadcast live from the lecture hall at Caltech.” The course objective is “machine learning that covers the basic theory, algorithms, and applications, that enables computational systems to adaptively improve their performance with experience accumulated from the observed data.” A book by the same title covering the same material is available.
I am attending (when schedule permits) because I believe that Machine Learning (ML) will (has) become a basic analysis technique of any complex system. However, I was surprised by a recent poll in KDnuggets that asked: “Can Machine Learning on Big Data replace Domain Expertise?” The majority (55%) felt that “there are many domains where machine learning cannot beat domain expertise”. However, Gregory Piatetsky-Shapiro (newsletter editor) argued that there are growing number examples where ML of Big Data outperform domain expertise. Many of the Knowledge Discovery (KD) competitions over the past ten years confirmed this.
I believe that successful applications of ML will involve a synthesis of ML with human domain expertise. The ML component will provide hints and basis instrumentation. However, humans will provide judgment and insights based on their domain expertise. Could a naive domain expert use ML functionality to perform useful analyses? Of course. However, a savvy domain expert could leverage ML much more.