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Lecture
Linear Regression Optimization
Description
This lecture covers the concept of linear regression, matrix multiplication, the D > N problem, cost functions like Mean Square Error (MSE) and Mean Absolute Error (MAE), and the trade-off between statistical and computational properties. It also discusses convexity, outliers, different optimization algorithms like grid search, and the challenges of optimization landscapes in machine learning.
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