CS 521 FML

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.

Emerging ML models (like deep neural networks) tend to be complex, fragile, non-robust, and uninterpretable. This makes it extremely challenging to build reliable real-world systems that incorporate ML components. We need trustworthy ML as well as robust system design to achieve end-to-end correctness of systems. In this course, we will study recent developments at the intersection of formal methods (FM), programming languages (PL) and machine learning (ML) research towards the development of trustworthy AI-based systems. Some topics planned covered for the course are: 1. Formal Verification of ML models using abstraction and constraint solvers 2. Symbolic explanations of deep neural networks 3. Training ML models with Logic and Knowledge 4. Learning symbolic concepts (code, program synthesis, invariants) 5. Proving correctness of systems with ML components 6. Neurosymbolic machine learning For up-to-date information about CS course restrictions, please see the following link: htt