There are major differences between machine learning, especially deep learning, and traditional statistics. In particular, the focus on learning nonlinear hiera

There are major differences between machine learning, especially deep learning, and traditional statistics. In particular, the focus on learning nonlinear hierarchical representations from raw data has enabled deep learning to make major advances in several applications beyond what traditional statistics could do. A major benefit of Deep Learning is that it can learn without manual feature selection (i.e., Representation Learning). Here's a good article: http://www.sciencedirect.com/science/article/pii/S2405918816300459 http://www.sciencedirect.com/science/article/pii/S2405918816... To your other point, DeepMind's Atari-playing neural networks figured out strategies that their programmers were not aware of. Another example is AlphaGo 2017's tactics used in its self-plays that perplexed master Go players worldwide because they were not in any Go books or records in history but they are hugely effective and the pros are now learning from them.