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Logistic Regression and Ensemble Learning - Bagging and Boosting - AdaBoost

Software Engineering Courses - SE Courses via YouTube

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This lecture introduces logistic regression for classification and ensemble learning, then explains bagging and boosting, including bootstrap sampling, weighted voting, AdaBoost, and decision stumps. It assumes familiarity with a programming language or an AI/ML tool.

Syllabus

Introduction
Probability
Classification
Regression vs Classification
Ensemble Learning
Benefits of Ensemble Learning
Independent Classifiers
Pros Cons
Randomness
When does bagging work
Boosting
Strong vs Weak Learners
Basic Algorithm Training
Weighted Vote
normalizing constant
AdaBoost
Strong NonLinear Classifier
Decision Stumps

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Software Engineering Courses - SE Courses

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