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The fundamental goal of machine learning is to construct a function that can generalize beyond examples in the training data
 a learning problem can abstractly be broken down into three components: Representation Evaluation Optimization
 components can be combined in order to find a fit learning algorithm for some use case
 error in learning can be categorized as bias or variance
 bias is tightly producing the wrong results consistently, this is a result of overfitting
 variance is producing results that are close to correct, but with low accuracy
 Overfitting  the learner should perform equally against testing data and new data, otherwise it is overfit
 every learner must have underlying assumptions and knowledge of the data, because no learner can generalize beyond the training data for random boolean functions

No free lunch theorem implies that a learning algorithms can't beat random guessing for random vector functions
 Multi testing problem  so many hypotheses tests that it is statistically probable that there are anomalies
 this can be mitigated by applying weighting to account for high volumes of hypothesis tests