WEEK-5 (15/02/2021 - 19/02/2021 )

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 In this week we were taught about Bayesian belief networks, Naive Bayes and we had given coursework to implement Linear Regression using NumPy.

Bayesian Belief Network: Bayesian Belief Network or Bayesian Network or Belief Network is a Probabilistic Graphical Model (PGM) that represents conditional dependencies between random variables through a Directed Acyclic Graph (DAG). Bayesian Networks are applied in many fields. For example, disease diagnosis, optimized web search, spam filtering, gene regulatory networks, etc. And this list can be extended. The main objective of these networks is trying to understand the structure of causality relations. To clarify this, let’s consider a disease diagnosis problem. With given symptoms and their resulting disease, we construct our Belief Network and when a new patient comes, we can infer which disease or diseases may have the new patient by providing probabilities for each disease. Similarly, these causality relations can be constructed for other problems and inference techniques can be applied to interesting results.

Naive Bayes: It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

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