Hello All, Welcome to my free Mathematical Foundations of Machine Learning bootcamp series. When we say Machine Learning, what does it actually mean? A machine that learns? Too vague. According to famous professor Tom Mitchell, a computer program is said to learn from experience E, with respect to some class of Tasks T, and Performance measure P, if its performance on tasks, as measured by P, improves with experience E. By swapping the nature of tasks T, the way we measure Performance P, to evaluate, we can subsume many kinds of ML problems. Also ML problems are analyzed well, when we view it from the lens of Probabilistic perspective, that is unknown quantities are endowed with probability distributions, and treated as Random variables. The interesting thing is Random variables are neither random nor variable. Probabilistic Approach also serves as the optimal approach to decision making under uncertainty. In this video, you get a sense of what ML actually is, if you have also wondered about it. submitted by /u/Negative_War_65
Originally posted by u/Negative_War_65 on r/ArtificialInteligence
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