CAL

## Tweedie GLM Interpretations and Stochastic Loss Development

I recently had the opportunity to participate in UC Berkeley's Actuarial case competition, hosted by the Cal Actuarial League. The topics of this case competition are Property & Casualty and General Analytics. The full case competition debrief is available here (.pdf), and a copy of the starter files is available here (.zip). Students were allotted roughly 2 weeks (Oct 23 - Nov 8) to complete the following tasks. Property & . . .

• Daniel Suryakusuma
Berkeley

## Numerical Analysis, Predictor Corrector Methods, and Iterative Improvement

The study of numerical methods and machine algorithms for systems modeling and computations exhibits widely applicable themes reaching far and beyond delivering outputs to calculator operations. Such themes and concepts are made explicit in Numerical Analysis (Math 128A) at UC Berkeley, which I had the pleasure of taking under the instruction of Professor John Strain. Because lectures by Strain are orthogonal (but complementary) to many ideas in three textbooks used . . .

• Daniel Suryakusuma
Berkeley

## Uniform Approximation, Bernstein Polynomials (Real Analysis)

Motivation for Bernstein Polynomials:A canonical method to approximating a function is by polynomial interpolation at a specified number of points. We study the asymptotic behavior of particular interpolation schemes in Math 128A, Numerical Analysis. However, we note that interpolation can yield wildly inaccurate results near the endpoints of interpolation (Runge Phenomenon), so we may instead want a sequence of polynomials that approximates our desired continuous function uniformly. As it . . .

• Daniel Suryakusuma
Berkeley

## Linear Algebra

This Spring 2019 semester I've completed the second course in Berkeley's Linear Algebra series (Math 110) with Professor Olga Holtz. Traditionally at Cal, students' first encounter with linear algebra takes form of computational linear algebra (Math 54), with a heavy focus on factorization and applications in solving linear problems. In this course, we take a purist approach (as done by course textbook author Axler) to not employ determinants of matrices . . .

• Daniel Suryakusuma