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here again next we have Jonathan Schafer who's speaking about a direct some results on the information complexity of learning hi and thank you and today I want to talk with you about a joint work I did together with you Donna home and I want to start off my talk with a basic simple I think fairly central intuition in learning saving and the intuition is that learning and data compression are very closely related tasks they might actually be equivalent in some senses and you can think about it this way if you want to compress some data set what you're basically going to do is you're going to look for patterns in that data set sit repeat and you're going to use those patterns to create a more concise or presentation on the other hand in learning what you want to do is you want to identify patterns in a training set and hopefully you can use those patterns to make predictions about an unseen test set and this is also related to the Safa chol idea of Occam&a

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