CS 521 LCU

Advanced topics in building and verifying software systems, selected from areas of current research such as: model checking and automated verification, testing and automated test generation, program synthesis, runtime verification, machine learning and its applications in the design of verified systems, formal analysis of machine learning algorithms, principles of programming languages and type systems. Course Information: May be repeated if topics vary. Credit is not given towards a degree from multiple offerings of this course if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 374 or ECE 374; CS 421. Additional prerequisites or corequisites may be specified each term. See section information.

Title: Approximate & Probablistic Computing. Description: The current drive for energy-efficiency has made approximation a key concept in designing and implementing software in various areas, such as data analytics, machine learning, mobile computing, multimedia processing, and engineering simulations. This course will focus on programming language foundations and system-level techniques for representing noise in program's data and reasoning about profitable tradeoffs between accuracy, reliability, and energy consumption. In addition to selected algorithmic-level approximations, we will study (i) programming languages that natively operate on probabilistic and/or uncertain data, (ii) compilers and programming systems that automatically approximate programs while verifying or testing the accuracy of optimized programs, (iii) techniques for verification and testing of probabilistic applications, and (iv) case studies of end-to-end systems that operate under strict resource constraints. P