Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.
Explores the theory of quasi-convexity in variational problems from continuum mechanics, discussing its principles, applications, and relationship with convexity.
Covers inductive propositions in Coq, focusing on evaluation rules for arithmetic expressions and their applications in defining partial and non-deterministic functions.
Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.
Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.
