In the last decade, Machine Learning has experienced a dramatic increase of performance. This has corresponded to an understandable hype especially for the remarkable results achieved in some cases.
Practitioners of Scientific Disciplines have become interested in utilizing new Machine Learning techniques, and have sometimes started doing so with mixed success.
In this talk I will briefly describe some of the common Traps, Pitfalls and Misconceptions of Machine Learning as relevant to the Scientific Discipline, and especially how to avoid them.
Attachment | Size |
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MLPitfalls_SEA2019.pdf | 2.28 MB |