Global Arc

The principal algorithms and concepts associated with translator systems. Topics include lexical analysis, syntactic analysis, parsing techniques, symbol table management, code generation and optimization, run time system design, implementation issues related to programming language design. Course will include a large-scale programming project utilizing the above topics. Three lectures. Prerequisites: 217 and 226 or instructor's permission.

This course is a broad introduction to different machine learning paradigms and algorithms and provides a foundation for further study or independent work in machine learning and data science. Topics include linear models for classification and regression, support vector machines, clustering, dimensionality reduction, deep neural networks, Markov decision processes, planning, and reinforcement learning. The goals of this course are three-fold: to understand the landscape of machine learning, how to compute the math behind techniques, and how to use Python and relevant libraries to implement and use various methods.

An introduction to the principles of typed functional programming. Programming recursive functions over structured data types and informal reasoning by induction about the correctness of those functions. Functional algorithms and data structures. Principles of modular programming, type abstraction, representation invariants and representation independence. Parallel functional programming, algorithms and applications.

The practice of programming. Emphasis is on the development of real programs, writing code but also assessing tradeoffs, choosing among design alternatives, debugging and testing, and improving performance. Issues include compatibility, robustness, and reliability, while meeting specifications. Students will have the opportunity to develop skills in these areas by working on their own code and in group projects. Two 90-minute lectures. Prerequisites: 217 and 226 (as corequisite).

COS 333I offers AB COS concentrators an option for completing one semester of independent work. As such, it does not carry course credit but it does follow the structure and format of the course COS 333. By taking this course not-for-credit, students in COS 333I will have the opportunity to develop their programming skills by working on their own code and in group projects to satisfy the JIW requirement for the semester in which they are enrolled. The cumulative grade for this work will be reflected on students' transcripts as COS 981.

This course introduces algorithms for analyzing DNA, RNA, and protein, the three fundamental molecules in the cell. Students will learn algorithms on strings, trees, and graphs and their applications in: sequence comparison and alignment; molecular evolution and comparative genomics; DNA sequencing and assembly; recognition of genes and regulatory elements; and RNA structure and protein interaction networks. Students will also implement algorithms and apply them to biological data.

Computer science--and its core notion of efficient computations--provides a new way of looking at the world by bringing a quantitative perpective to fundamental notions such as randomness, proofs, games, and learning. The radical insight of efficient computations is the assumption of "bounded computational power"--that all observers (human, machine, or nature itself) in these scenarios should be considered computationally limited. This marriage of computational complexity with science and mathematics has led to startling ideas such as: zero-knowledge proofs, cryptography, pseudo-randomness, learning (theoretical AI), and quantum computing.

An elementary introduction to quantum mechanics, especially aimed at students interested in interdisciplinary areas like nanoscience and quantum computing. Using "qubits" (basic building blocks of quantum computation, information, and cryptography), we give simple treatments of standard QM topics such as superposition, tensor products, Bell's inequalities and Heisenberg uncertainty principle, as well as "advanced" topics like multi-particle systems and spin. We also discuss the computational power of quantum computers and cryptosystems as well as their possible lab implementations.

An introduction to computer architecture and organization. Instruction set design; basic processor implementation techniques; performance measurement; caches and virtual memory; pipelined processor design; design trade-offs among cost, performance, and complexity. Two 90-minute classes, one self-scheduled hardware laboratory. Prerequisites: COS 217.

Offered in the fall, juniors are provided with an opportunity to concentrate on a "state-of-the-art" project in computer science. Topics may be selected from suggestions by faculty members or proposed by the student. B.S.E. candidates only.