Programming and problem solving using Python. Emphasizes principles of software development, style, and testing. Topics include procedures and functions, iteration, recursion, arrays and vectors, strings, an operational model of procedure and function calls, algorithms, exceptions, object-oriented programming.

Programming and problem solving using Python. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays, strings, recursion, object-oriented programming, algorithms, and data handling and visualization.

Fall, Spring, Summer. 4 credits. Student option grading. C. Muhlberger, A. Myers. Intermediate programming in a high-level language and introduction to computer science. Topics include object-oriented programming (classes, objects, subclasses, types), graphical user interfaces, algorithm analysis (asymptotic complexity, big “O” notation), recursion, testing, program correctness (loop invariants), searching/sorting, data structures (lists, trees, stacks, queues, heaps, search trees, hash tables, graphs), graph algorithms. Java is the principal programming language.

Fall. Not offered: 2023-2024. Next offered: 2024-2025. 4 credits. Student option grading (no audit). N Foster. Intermediate software design and introduction to key computer science ideas. Topics are similar to those in CS 2110 but are covered in greater depth, with more challenging assignments. Topics include object-oriented programming, program structure and organization, program reasoning using specifications and invariants, recursion, design patterns, concurrent programming, graphical user interfaces, data structures as in CS 2110, sorting and graph algorithms, asymptotic complexity, and simple algorithm analysis. Java is the principal programming language.

Covers the mathematics that underlies most of computer science.

Covers the mathematics that underlies most of computer science. This course is an honors version of CS 2800.

Introduction to computer organization, systems programming and the hardware/ software interface. Topics include instruction sets, computer arithmetic, datapath design, data formats, addressing modes, memory hierarchies including caches and virtual memory, I/O devices, bus-based I/O systems, and multicore architectures. Students learn assembly language programming and design a pipelined RISC processor.

For description, see ECE 3140.

Introduction to the design of systems programs, with emphasis on multiprogrammed operating systems.

CS 4414 exposes students to programming applications at the systems level.

Covers the mathematical foundations for access to information. Topics include high dimensional space, random graphs, singular value decomposition, Markov processes, learning theory, and algorithms for massive data.

Introduction to probability theory, which prepares the student to take MATH 4720. The course begins with basics: combinatorial probability, mean and variance, independence, conditional probability, and Bayes formula. Density and distribution functions and their properties are introduced. The law of large numbers and the central limit theorem are stated and their implications for statistics are discussed.

Gives students a working knowledge of basic probability and statistics and their application to engineering. Includes computer analysis of data and simulation. Topics include random variables, probability distributions, expectation, estimation, testing, experimental design, quality control, and regression.

Probability theory is a mathematical discipline that allows one to reason about uncertainty: it helps us to predict uncertain events, to make better decisions under uncertainty, and to design and build systems that must operate in uncertain environments. This course will serve as an introduction to the subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference in the presence of uncertainty. Topics include probability models, combinatorics, countable and uncountable sample spaces, discrete random variables, probability mass functions, continuous random variables, probability density functions, cumulative distribution functions, expectation and variance, independence and correlation, conditioning and Bayess rule, concentration inequalities, the multivariate Normal distribution, limit theorems (including the law of large numbers and the central limit theorem), Monte Carlo methods, random processes, and the basics of statistical inference. Applications to communications, networking, circuit design, computer engineering, finance, and voting will be discussed throughout the semester.

This course provides an introduction to probability and parametric inference. Topics include: random variables, standard distributions, the law of large numbers, the central limit theorem, likelihood-based estimation, sampling distributions and hypothesis testing, as well as an introduction to Bayesian methods. Some assignments may involve computation using the R programming language.

Provides an introduction to statistical inference and to principles of probability. It includes descriptive statistics, principles of probability, discrete and continuous distributions, and hypothesis testing (of sample means, proportions, variance). Regression analysis and correlation are introduced.