Naive bayes Study guides, Class notes & Summaries

Generative Learning algorithms Gaussian discriminant analysis. Naive Bayes. Laplace Smoothing.

Outline Naive Bayes Laplacesmoothing Event Models Kernel Methods

FULL NOTES ON ARITFICIAL INTELLIGENCE AND MACHINE LEARNING. COVERED ALL ALGORITHM OF MACHINE LEARNING WITH REAL WORLD EXAMPLES WHICH MAKES BETTER CLARITY.

Gaussian discriminant analysis & it is model Naive Bayes.

 Bayes Theorem  MAP, ML hypotheses  MAP learners  Minimum description length principle  Bayes optimal classier  Naive Bayes learner  Example: Learning over text data  Bayesian belief networks  Expectation Maximization algorithm 125 lectur

 Bayes Theorem  MAP, ML hypotheses  MAP learners  Minimum description length principle  Bayes optimal classier  Naive Bayes learner  Example: Learning over text data  Bayesian belief networks  Expectation Maximization algorithm

● Organisational Matters ● k-Nearest Neighbour Distance is the Essential Ingredient Inductive Bias Extensions of k-Nearest Neighbour ● Probability Theory ● Naive Bayes Problem Estimating Probabilities Solution: Independence Assumption

This contains exercises for all the LAB sessions. The exercises come from various old exams, ranging from 2012 to 2018. The exercises are grouped by each subject: Naive Bayes, Decision Trees, Clustering, SQL and Association rules. Some exams did not provide answers so I answered them. Those questions are marked so you know that the answers may or may not be correct.

Summary including lectures and video lectures. Also includes a few parts on SQL.

Summary of NL Annotations for ML lectures. Summary of the lectures.