A survey of modern programming language principles, focused on
implementing ideas. Topics include interpreters, type-inference
algorithms, program analyses, program verification, and garbage collection.

People are able to infer the characteristics of a scene or object from an image of it. In this course, we will study what is involved in building artificial systems which try to infer such characteristics from an image. Topics include: Basics of image formation - the effect of geometry, viewpoint, lighting and albedo on image formation. Basic image operations such as filtering, convolution and correlation. Frequency representations of images. The importance of scale in images. Measurements of image properties such as color, texture, appearance and shape.

This course provides a rigorous introduction to mathematical logic from an algorithmic perspective. Topics include Propositional Logic: Horn Clause Satisfiability and SAT Solvers; First Order Logic: Soundness and Completeness of Resolution; Compactness Theorem; Automatic Theorem Proving; Model Checking. Students will learn the theory behind and use the following systems, languages, and tools: the Coq theorem prover, Datalog, the NuSMV Model Checker, and SAT and SMT solvers.

An introduction to fundamental concepts in the design and implementation of computer communication networks, their protocols, and applications. Topics include: layered network architectures, applications, network programming interfaces (e.g., sockets), transport, congestion, routing and data link protocols, local area networks, emerging high-speed networks, network management, and network security. Examples drawn primarily from the Internet (e.g., TCP, UDP, and IP) protocol suite. A rudimentary understanding of computer architecture and operating systems helpful.

Practical experience in the management of software development projects. Managing people, coaching, mentoring, planning, scheduling, evaluation, negotiation, and expectation management are explored. The focus is on the power of communication.

Introduction to mathematical logic: propositional logic, first-order logic, completemess, emphasizing applications to computer science including SAT solvers, model checking, and resolution theorem proving.

Embedded Computational Systems is a hands-on laboratory for building integrated systems. We develop analytical skills and discuss sensors, motor drivers, I/O, control logic, and operating systems for embedded applications. Students build several individual projects and invent one integrated, semester-long team project (robot insects and stigmergy, Trinity fire fighting challenge).

Computing has become data-driven, and databases are now at the heart of commercial applications. The purpose of this course is to provide a comprehensive introduction to the use of data management systems within the context of various applications. Some of the covered topics include application-driven database design, schema refinement, implementation of basic transactions, data on the web, and data visualization. This course counts as a CS Elective toward the CMPSCI major (BA/BS).

Mathematical methods for dealing with uncertainty. Basic counting problems. Discrete probablitlity, expected value and variance. Random number generators and Monte Carlo simulation. Bayesian reasoning, naive Bayes classifiers. Markov processes. Programming projects.

A brief introduction to the C programming language for students with a good working knowledge of Java and data structures. This course is good preparation for CMPSCI 230 and courses that use C and C++. Prerequisites: CMPSCI 187 or ECE 242 or permission of instructor. This course is for CMPSCI minors and majors only, but it does not count towards either degree.