18C5T13 Study Guide - Final Guide: Logistic Regression, Statistical Shape Analysis, Unit
11 Aug 2021
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Unit 5 unit- v:probabilistic models: the normal distribution and its geometric interpretations, Probabilistic models for categorical data, discriminative learning by optimising conditional likelihoodprobabilistic models with hidden variables. features: kinds of feature, feature transformations, feature construction and selection. Discriminative learning by optimising conditional likelihood probabilistic models with hidden variables. Bias are the simplifying assumptions made by a model to make the target function easier to learn. Low bias: suggests less assumptions about the form of the target function. High-bias: suggests more assumptions about the form of the target function. Variance is the amount that the estimate of the target function will change if different training data was used. Low variance: suggests small changes to the estimate of the target function with changes to the training dataset. High variance: suggests large changes to the estimate of the target function with changes to the training dataset. For converting weak learner to strong learner combine the prediction of each.
