Optimization for Machine Learning (Fall 2026)

Five homework assignments, each worth 4% of the course grade.

HomeworkTopicsDue dateFiles
Homework 1Convexity, stationary points, local minima, and global minimaSep 4, 11:59 pmPDF
Homework 2Gradient descent, acceleration, Newton’s method, conditioning, step sizes, and restartSep 21, 11:59 pmPDF
Homework 3Constrained optimization, duality, KKT conditions, SVMs and kernels, gradient projection, and Newton’s methodOct 2, 11:59 pmPDF
Homework 4
Homework 5

Notes

  • Submit your answers as a PDF on Canvas.

  • Discussions with others are allowed, but all answers must be completed independently.

  • Late submissions are not accepted.

  • Include an AI Use Statement documenting any AI assistance, or state that no AI tools were used. You are responsible for independently understanding and verifying your work. See the syllabus for the full policy.