Learn about creating strong machine learning models through boosting techniques and ensemble methods in this university lecture that explores how to combine multiple weak learners or basic rules of thumb into powerful predictive systems. Delve into the theoretical foundations and practical applications of boosting algorithms, understanding how they iteratively improve model performance by focusing on previously misclassified examples. Discover various ensemble methods that combine multiple models to achieve better predictive accuracy than any single model could achieve alone.