Semantics

Text classification

Language Modeling

Vector Embeddings

PyTorch

Finite-state machine

Sequence Labelling

Machine Translation

Sequence-to-sequence (Seq2Seq)

Morphology

Syntax

Conditional Random Fields (CRF)

Linguistic Structure Prediction

Neural Machine Translation

Multinomial Logistic Regression

Weighted Finite-State Transducers

CSE 447 and 517 Natural Language Processing University of Washington Winter 2022 This course provides a comprehensive overview of Natural Language Processing (NLP), including core components like text classification, machine translation, and syntax analysis. It offers two project types: implementation problem-solving for CSE 447, and reproducing experiments from recent NLP papers for CSE 517.  Natural language processing (NLP) seeks to endow computers with the ability to intelligently process human language. NLP components are used in conversational agents and other systems that engage in dialogue with humans, automatic translation between human languages, automatic answering of questions using large text collections, the extraction of structured information from text, tools that help human authors, and many, many more. This course will teach you the fundamental ideas used in key NLP components. It is organized into several “greatest hits” topics, each with a more-or-less self-contained lecture and associated readings, problems, and implementation exercises.  The courses are similar in breadth and use the same lecture content. The projects are quite different; 447’s project is a predefined implementation problem that gives teams freedom in developing a solution. It is designed to encourage iterative improvement and an understanding of inherent tradeoffs in building an NLP system. 517’s project is more research-oriented; it asks teams to reproduce experiments in recently published NLP papers. Teams have great flexibility in the choice of a paper to reproduce.

Additionally, there will be differences in the assignments.

CSE 447 and 517 Natural Language Processing

Natural Language Processing

Regular Expressions from Axioms

Induction

Propositional Encodings

SAT Certificates

Contracts

Dynamic Logic

Convergence

DPLL

Blasting & Quantifiers

Data structure

Compositional Reasoning

Predicate transformer semantics

Sound Transformations

Temporal logic

Ghosts

Loops

Sequent Calculus

Satisfiability Modulo Theories (SMT)

Array (data structure)

Resolution

Equality and Arrays

15-414 Bug Catching: Automated Program Verification Carnegie Mellon University Spring 2022 This course is about software verification, with the goal of writing bug-free code. Students will learn to formalize program correctness, write verified code, and use automated tools for verification. It explores the principles behind verification tools, logical specifications, and deductive reasoning. Previous knowledge in program correctness reasoning is beneficial. High-profile bugs continue to plague the software industry, leading to major problems in the reliability, safety, and security of systems. This course teaches students how to write bug-free code through the process of software verification, which aims to prove the correctness of a program with respect to a mathematical specification. Along the way, students will learn how to:

- Specify correct program behavior
- Prove the correctness of their code
- Use formal semantics to reason about the soundness of proof rules
- Write practical and efficient verified code
- Use decision procedures and model checkers to reduce verification effort

Students will learn the principles and algorithms behind automated verification tools, and understand their practical limitations while gaining experience writing verified, machine-checked code that solves real problems. **Identify and formalize program correctness.** In order to reason about the existence of bugs in code, and ultimately justify their absence, we need to state what it means for the code to be correct. Students will learn how to formalize correctness using logical specifications that give precise meaning to the program's intended behavior.

**Understand how to write correct software from the ground up.** Writing good specifications of program correctness is essential to showing that there aren't any bugs in the code, but it is also useful for writing good code from the start. Students will develop experience reasoning about correctness as they implement functionality, learning how to write code that establishes and preserves properties and invariants needed to meet a target specification.

**Apply rigorous reasoning to program correctness.** Program verification is the process of mathematically proving that all possible behaviors of a given program adhere to its specification. Students will learn how to use deductive reasoning and state space exploration to realize this goal, and gain experience verifying their own code against logical correctness specifications.

**Learn to use automated tools to aid in verification.** Formally verifying real code is challenging, but given the scale of modern software, can also be tedious and labor-intensive. A number of tools exist for automating key parts of the verification process, notably deductive verification tools and model checkers. Students will learn how to use these tools when developing correct software, and gain experience applying them to non-trivial implementations.

**Understand how verification tools work.** Verification tools are not perfect, and will never be able to scale universally to all software development needs. Understanding the principles and techniques that these tools use is necessary for evaluating when it is appropriate to use a particular tool, and for addressing a tool's limitations when they arise in practice.  There is no textbook for this class, and detailed lecture notes will be posted to the website within a few days after each class. Keep in mind that lecture notes may not contain everything we cover in class. Comments and corrections are welcome, please give them to the course staff on Piazza.

15-414 Bug Catching: Automated Program Verification

Software Engineering

Semantics

2 courses cover this concept

CSE 447 and 517 Natural Language Processing

15-414 Bug Catching: Automated Program Verification