machine learning day 1—class notes

entropy: natural tendency of nature toward chaos (milk stirs into coffee forever); randomness is the likely outcome of things

math of machine learning is rooted in contorting the physical world to imitate what we want it to represent (bringing organization to entropy)

rationalism: knowledge comes from truth; anything we know, we know through the ether (or the gods)
empiricism: all knowledge is derived from experience; you can’t know anything without experiencing it—no truth, just opinions

kant integrated both by saying that minds have been constructed in a similar way, bodies are born in a similar way

time, space, causality; we have to operate in a spacial, temporal, causal world; truth comes from these conditions

truth in machine learning outputs is relative to its experience; not objectively correct

pixels > face too great of a dissonance, start with surfaces, edges, shapes

machine learning = induction

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