Results for Programming Abstractions

This course covers the fundamentals of functional programming and algebraic type systems, and explores a selection of related programming paradigms and current research. Haskell is taught and used throughout the course, though much of the material is applicable to other languages. Material will be covered from both theoretical and practical points of view, and topics will include higher order functions, immutable data structures, algebraic data types, type inference, lenses and optics, effect systems, concurrency and parallelism, and dependent types. Prerequisites: Programming maturity and comfort with math proofs, at the levels of CS107 and CS103.

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent.

This enrichment add-on is a companion course to CS106B to explore additional topics and go into further depth. Specific topics to be announced per-quarter; past topics have included search engines, pattern recognition, data compression/encryption, error correction, digital signatures, and numerical recipes. Students must be co-enrolled in CS106B. Refer to cs106m.stanford.edu for more information.

Intensive version of 106B for students with a strong programming background interested in a rigorous treatment of the topics at an accelerated pace. Significant amount of additional advanced material and substantially more challenging projects. Some projects may relate to CS department research. Prerequisite: excellence in 106A or equivalent, or consent of instructor.

Automated logical reasoning has enabled substantial progress in many fields, including hardware and software verification, theorem-proving, and artificial in- telligence. Different application scenarios may require different automated rea- soning techniques and sometimes their combination. In this course, we will study widely-used logical theories as well as algorithms for answering whether formu- las in those theories are satisfiable. We will consider state-of-the-art automated reasoning techniques for propositional logic, first-order logic, and various first- order theories, such as linear arithmetic over reals and integers, uninterpreted functions, bit-vectors, and arrays. We will also consider ways to reason about combinations of those theories. Topics include: logical foundations, SAT-solving, techniques for first-order theorem proving, decision procedures for different first- order theories, theory combination, the DPLL(T) framework, and applications of automated reasoning in program analysis and hardware verification. Prerequisites: CS154 Introduction to the Theory of Computation, or CS106b Programming Abstractions and CS103 Mathematical Foundations of Computing, or consent of instructor