Announcement

• HW7 (Q1-7) due today @ 11:59pm on Gradescope/BruinLearn.

• Please send me any topics and questions you would like me to review by Wed Dec 4 @ 5pm.

• Concluding remarks. Three pillars of machine learning: analysis/probability/statistics, linear algebra/optimization, and computing/algorithms. Stephen Boyd’s advice on graduate studies.

• Course evaluation: MyUCLA.

Feedback on HW6 (thanks to Tomoki)

• Q2. Some students immediately claimed $N(A)=N(A’)$ without using the fact that $A$ is normal.

• Q3.2. Half of the students showed incomplete solutions without using backward/forward substitution or the induction method.

• Q3.5. $A$ has to be both upper and lower triangular due to part (ii) and orthogonality, meaning $A$ must be diagonal.

• Q10.3. Use the fact that eigenvectors associated with distinct eigenvalues are linearly independent.

Today

• SVD (cont’d).

• MV calculus and optimization.