This course is available on the BSc in Business Mathematics and Statistics, BSc in Mathematics and Economics, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.
This course is available on the BSc in Business Mathematics and Statistics, BSc in Mathematics and Economics, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.
This course is delivered through a combination of classes and lectures totalling a minimum of 32 hours across Lent and Summer term. This year, apart from pre-recorded lecture videos, there will be a weekly live online session of an hour. Depending on circumstances, classes might be online.
Students will be expected to produce 8 exercises in the LT. Written answers to set problems will be expected on a weekly basis.
Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press. Shalev-Shwartz, S. & Ben-David, S. (2004). Understanding Machine Learning: From Theory to Algorithms.?. Cambridge University Press. Nesterov, Y. (2018). Lectures on convex optimization (Vol. 137). Springer. Blum, A., Hopcroft, J., & Kannan, R. (2020). Foundations of data science. Cambridge University Press. Vishnoi, N. (2018). Algorithms for Convex Optimization (2021).?Cambridge University Press.
Exam (90%, duration: 2 hours) in the summer exam period. Coursework (10%) in the LT. The coursework will comprise two problem sets during term time.