From optimization, to convex optimization, to first order optimization, to gradient descent, to accelerated gradient descent, to AdaGrad, to Adam.

This is my note for ISYE 8803. This course focuses on analysis of high-dimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in high-dimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed.

This is my notes on Georgia Tech's ISYE 6669: Deterministic Optimization. Optimization is the process of adjusting a system to achieve the best possible performance or outcome. Deterministic (non-stochastic) optimization is a mathematical approach to finding the best solution to a problem by systematically searching the solution space for the optimal outcome. The optimization process is based on a set of deterministic (i.e., non-random) rules and algorithms, and the result of the optimization process is unique and repeatable.

Analytics is extremely relevant in all aspects of ride-hailing. In this project, I merely covered a few use cases, with one or two relevant models. Even with this brief exploration, I can conclude that analytics can lead to better outcomes for both drivers and passengers.