Organization and scheduling of software engineering projects, structured programming, and design. Each team designs, codes, and debugs program components and synthesizes them into a tested, documented program product.
Introduction to techniques and applications of modern artificial intelligence. Combinatorial search, probabilistic models and reasoning, and applications to natural language understanding, robotics, and computer vision.
This pilot course introduces software engineering best practices in the context of full-stack web development with a high emphasis on team collaboration. The course is modeled after an internship and teams of four ultimately aim to produce production-quality features for a community application
Principles of mobile applications, mobile OS, mobile networks, and embedded sensor systems. Coursework includes programming assignments, reading from recent research literature, and a semester long project on a mobile computing platform (e.g., Android, Arduino, iOS, etc.).
Introduction to the theory of computation. Finite automata, regular languages, pushdown automata, context-free languages, and Turing machines. Undecidable problems.
Formal specification and verification of programs. Techniques of algorithm analysis. Problem-solving paradigms. Survey of selected algorithms.
Introduction to theory and methods of machine learning including classification; Bayes risk/rule, linear discriminant analysis, logistic regression, nearest neighbors, and support vector machines; clustering algorithms; overfitting, estimation error, cross validation.
Placement of data on secondary storage. File organization. Database history, practice, major models, system structure and design.
Introduction to computer organization and design. Students will be introduced to the conceptual design of a basic microprocessor, along with assembly programming. The course includes fundamental concepts such as binary numbers, binary arithmetic, and representing information as well as instructions. Students learn to program in assembly (i.e., machine) language. The course covers the fundamentals of computer hardware design, transistors and logic gates, progressing through basic combinational and sequential components, culminating in the conceptual design CPU.
Students will learn how to reason about how their code is structured, identify whether a given structure is effective in a given context, and look at ways of organizing units of code that support larger programs. In a nutshell, the primary goal of the course is to equip students with tools and techniques that will help them not only in later courses in the major but also in their careers afterwards.
This is the first course in the introductory systems sequence. Students enter the course having taken an introductory programming course in a high-level programming language (COMP 110) and a course in discrete structures. The overarching goal is to bridge the gap between a students' knowledge of a high-level programming language (COMP 110) and computer organization (COMP 311).
This course will teach you how to organize the data used in computer programs so that manipulation of that data can be done efficiently on large problems and large data instances. Rather than learning to use the data structures found in the libraries of programming languages, you will be learning how those libraries are constructed, and why the items that are included in them are there (and why some are excluded).
Introduces students to programming and data science from a computational perspective. With an emphasis on modern applications in society, students gain experience with problem decomposition, algorithms for data analysis, abstraction design, and ethics in computing. No prior programming experience expected. Foundational concepts include data types, sequences, boolean logic, control flow, functions/methods, recursion, classes/objects, input/output, data organization, transformations, and visualizations.
Introduction to Markov chains, Poisson process, continuous-time Markov chains, renewal theory. Applications to queueing systems, inventory, and reliability, with emphasis on systems modeling, design, and control.
Review of basic inference; two-sample comparisons; correlation; introduction to matrices; simple and multiple regression (including significance tests, diagnostics, variable selection); analysis of variance; use of statistical software.
Introduction to mathematical theory of probability covering random variables; moments; binomial, Poisson, normal and related distributions; generating functions; sums and sequences of random variables; and statistical applications.
Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory.
Development of basic skill set for data analysis from obtaining data to data carpentry, exploration, modeling, and communication. Topics covered include regression, clustering, classification, algorithmic thinking, and non-standard data objects (networks and text data).
Algebra of matrices with applications: determinants, solution of linear systems by Gaussian elimination, Gram-Schmidt procedure, and eigenvalues.
Introductory ordinary differential equations, first- and second-order differential equations with applications, higher-order linear equations, systems of first-order linear equations (introducing linear algebra as needed).
This course serves as a transition from computational to more theoretical mathematics. Topics are from the foundations of mathematics: logic, set theory, relations and functions, induction, permutations and combinations, recurrence.
This course investigates philosophical issues arising from advanced forms of technology, in particular artificial intelligence, virtual reality, and biological augmentation. We will consider questions about the dangers and benefits of AI, survival in non-biological ways, moral constraints on AI, the relationship between human and machine morality, and others.