Naive bayes - Study guides, Class notes & Summaries

Extensive, clear summary of the course 'Introduction to Machine Learning', based on elaborated notes made during the lecture, including graphs and pictures, connected to the lecture slides. The document is divided in 2 parts, the first part concerning the midterm material, the second part concerning the additional final exam material (everything is in this document).

Machine Learning summary for the 2nd year of artificial intelligence study. All chapters and lectures before the exam.

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

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

Outline Naive Bayes Laplacesmoothing Event Models Kernel Methods

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