Table of Contents

🎓 Intended learning outcomes

At the end of this lesson, students are expected to:

• understand the concept of error (in ML context)
• define MSE and RMSE
• formulate a loss function from basic principles
• define average loss/empirical risk
• formulate a linear regression problem
• convert linear regression equations from the standard form to vector form
• define convexity of a function and relate it to the optimization of linear regression models
• compute the gradient of a matrix-valued function
• apply knowledge of convexity to devise the ordinary least squares method
• know and be able to correctly use the notation conventions

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