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.

Introduction to ordinary and partial differential equations. Topics include: first-order equations (separable, linear, homogeneous, exact); mathematical modeling (e.g., population growth, terminal velocity); qualitative methods (slope fields, phase plots, equilibria, and stability); numerical methods; second-order equations (method of undetermined coefficients, application to oscillations and resonance, boundary-value problems and eigenvalues); and Fourier series. A substantial part of this course involves partial differential equations, such as the heat equation, the wave equation, and Laplace’s equation.

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.

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.

Covers fundamental chemical principles, including reaction kinetics, thermodynamics, and equilibrium. These principles are presented quantitatively and explored in the laboratory. Considerable attention is given to the quantitative calculations and techniques important for further work in chemistry.

Intensive systematic study of the laws and concepts of chemistry, with considerable emphasis on quantitative aspects. CHEM 2150 covers electronic structure of atoms, chemical bonding, thermodynamics, kinetics, and equilibrium. This course serves as an accelerated entry into organic chemistry in the Spring semester for students with a strong background in chemistry. Laboratory work covers qualitative and quantitative analysis, thermodynamics, kinetics transition metal chemistry, and spectroscopic techniques.

For majors in engineering (including bio-, civil, and environmental engineering), computer and information science, physics, earth and atmospheric science, and other physical and biological sciences who wish to understand the oscillation, wave, and quantum phenomena behind everyday experiences and modern technology including scientific/medical instrumentation. Covers the physics of oscillations and wave phenomena, including driven oscillations and resonance, mechanical waves, sound waves, electromagnetic waves, standing waves, Doppler effect, polarization, wave reflection and transmission, interference, diffraction, geometric optics and optical instruments, wave properties of particles, particles in potential wells, light emission and absorption, and quantum tunneling. With applications to phenomena and measurement technologies in engineering, the physical sciences, and biological sciences. Some familiarity with differential equations, complex representation of sinusoids, and Fourier analysis is desirable but not essential.

This course is divided into two parts. The larger segment of the course typically focuses on wave phenomena. Topics include: coupled harmonic oscillators, strings, sound and light waves, superposition principle, wave equations, Fourier series and transforms, diffraction and interference. The discussion is at the level of The Physics of Waves by Georgi. The second segment of the course covers thermodynamics and statistical mechanics at the level of Thermal Physics by Schroeder.

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.

First course in a three-semester introductory physics sequence. This course is taught in a largely 'flipped', highly interactive manner, with reading preparation and online reading quizzes required for class. Covers the mechanics of particles with focus on kinematics, dynamics, conservation laws, central force fields, periodic motion. Mechanics of many-particle systems: center of mass, rotational mechanics of a rigid body, translational & rotational equilibrium. Temperature, heat, the laws of thermodynamics. At the level of University Physics, Vol. 1, by Young and Freedman.

First in a three-semester introductory physics sequence. Explores quantitative modeling of the physical world through a study of mechanics. More mathematical than a typical mechanics course - for example, considers how choice of coordinate system (Cartesian, cylindrical, etc.) influences the nature of kinematical equations. Includes kinematics, dynamics, conservation laws, central force fields, periodic motion, and special relativity. At the level of An Introduction to Mechanics by Kleppner and Kolenkow, which assumes a strong mathematical foundation in calculus.

Second course in a three semester introductory physics sequence. The course emphasizes active learning during class. Video lectures are viewed before class; most class time is devoted to problem-solving. Topics include: electric forces and fields, electric energy and potential, circuits, magnetic forces and fields, magnetic induction, and Maxwell’s equations. Taught at a level somewhat higher than University Physics, Vol. 2, by Young and Freedman. The math prerequisite is essential: line, surface, and volume integrals are done routinely and occasional use is made of gradient, divergence, and curl.

Second in a three semester introductory physics sequence. Explores quantitative modeling of the physical world through a study of electricity and magnetism. More mathematical and abstract than a typical introductory electricity and magnetism course. Topics include electrostatics, behavior of matter in electric fields, circuits, magnetic fields, Faraday’s law, AC circuits, and electromagnetic waves. Makes substantial use of vector calculus. At the level of Electricity and Magnetism by Purcell.