Covers the general logistics, course rationale, prerequisites, organization, credits, workload, grading, and course content, including swarm intelligence, foraging strategies, and collective phenomena.
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.
Explores inverse design optimization in geometric computing, covering topics such as deployable structures, material-aware design, and programmed materials.
Explores self-organization in natural systems and foraging strategies of ants, including the Traveling Salesman Problem and Ant Colony Optimization algorithms.
Explores the design of flexible guidances, emphasizing the analysis of structures with orthogonal flexible joints and the optimization using flexible columns.
Explores the impact of big data, covering economic value, latency-sensitive and throughput-bound applications, graph analytics, and challenges in utilizing flash storage.
