Early machine learning used very small data sets. Data science often involves very large data sets. Work on 'scaling-up' to large data sets has concentrated on reducing the computational complexity of existing algorithms. We contend that this may not be appropriate, that learning from large data sets is fundamentally different to learning from small data sets and that different types of algorithm may be most effective.
