Critical Analysis of Scientific Data

PHYSICS 481

Data science is most commonly associated with topics in computer science. However, efficient algorithms, specific software packages, neural nets, and so on are only tools, and they are easily misused. In a research setting, working with data is primarily an exercise in critical thinking. The purpose of this interactive, hands-on course is to learn from mistakes by making them in a safe environment. After covering/reviewing probability theory, Bayesian inference, elements of information theory, and random matrix theory, the course will focus on case studies of real-world biological data, such as quantitative imaging data, nextgeneration sequencing (metagenomics), and neural recordings. These modules will involve the critical reading of research papers and working through puzzle-based assignments. The primary modules will be supplemented by shorter presentations on topics chosen by students. Fair warning: This is explicitly not a course on "big data" or machine learning, although students may choose to explore some of these topics in their presentations (required for credit). Experience with MatLab or Python strongly encouraged or will need to be acquired during the course. Open to undergraduates with prior programming experience and a quantitative background (Phys 197/198, Math 203 or similar; contact instructor if unsure). Experience with data or statistics not required. Course mimics a research environment, and undergraduates considering an academic research track are especially encouraged.
Course Attributes: FA NSM; BU SCI; AR NSM

Introduction to the Atomic Nucleus

PHYSICS 5036

Introduction to the interaction of radiation with mater, the production and decay of radioactive nuclides, the structure and properties of nuclei, and various applications of nuclear science (including nuclear power) are all presented. Prerequisites: General Chemistry and/or Physics, and prior or concurrent enrollment in either Chemistry 401 or Physics 217. Lectures will be in-person (if allowed) but a complete set of taped lectures will also be available. A weekly, in-person or remote, help session will be scheduled at a mutually agreed to time. There will be about 6 timed quizzes, one midterm and one final, all of which must be taken in-person on mutually agreed dates.
Course Attributes: FA NSM; AR NSM; AS NSM

Planets and Life in the Universe

PHYSICS 5330

In this course, we will explore the history, methods, outcomes, and broad impacts of exoplanet research and how these are connected to our search for life beyond planet Earth. Following an engaging contextual introduction at the beginning of the lectures, topics will be presented with an accessible mathematical treatment (e.g., geometrical derivations of the two-body transit problem). Prerequisite: Physics 191 and 192 or Physics 193 and 194.
Course Attributes: FA NSM; AS NSM; AS AN

Instructors

Quantum Mechanics

PHYSICS 5071

Origins of quantum theory, wave packets and uncertainty relations, Schroedinger's equation, eigenfunctions and eigenvalues, Schroedinger's equation in three dimensions, formalism of quantum mechanics, symmetry, spin and the periodic table, approximation methods for time independent problems, scattering, quantum statistics. Prerequisite: Physics 217, or permission of instructor.
Course Attributes: FA NSM; EN TU; EN SU; AR NSM; AS NSM; EN BME T2

Instructors

Statistical Mechanics and Thermodynamics

PHYSICS 5063

This course will discuss the thermodynamics of open and closed systems, kinetics and transport theory, and classical and quantum statistical mechanics. Prerequisite: Physics 217 or permission of instructor.
Course Attributes: FA NSM; AR NSM; AS NSM; AS AN

Instructors

Introduction to Computational Physics

PHYSICS 5027

What does it mean to solve a research problem using a computer? What is the difference between "someone ran a simulation" and an interesting research result? And what skills does it take? Familiarity with a programming language is, of course, essential, but that is only the beginning. This course will focus on the methodology of computational research, touching also on topics in numerical analysis, statistics and visualization. The format will combine lectures and hands-on experience, with emphasis on research-style small-group projects. Prerequisites:Prerequisite: Physics 191 - 192 or Phys 193 - 194 or Physics 197-198 or Phys 205 - 206, Calculus, and familiarity with a programming language.
Course Attributes: FA NSM; AR NSM; AS NSM

Instructors

Physical Science in 12 Problems

PHYSICS 5001

Exercises related to general chemistry, classical mechanics, quantum mechanics, statistical mechanics, thermodynamics, and kinetics, will be solved with numerical software. Each exercise will be accompanied by a lecture, a software template solving a problem and presenting a related take-home problem. The software will allow us to focus on, and treat in a transparent fashion, physical problems without the unworldly idealizations and contrivances found in textbooks. Prerequisites: General Chemistry and/or Physics, and prior or concurrent enrollment in either Chem 401 or Phys 217. The lectures will be in-person however a complete set of taped lectures will also be available. A remote help session will be scheduled at a mutually agreed to time. There are no quizzes, exams or a final.
Course Attributes: FA NSM; AR NSM; AS NSM

Instructors

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Electricity and Magnetism I

PHYSICS 5021

The first course in a two part series covering the classical theory of electricity and magnetism leading to the derivation an application of Maxwell's equations. Vector algebra and calculus, electrostatics and magnetostatics in vacuum and in materials, Coulomb's Law, the Biot-Savart law, Gauss' law, and Ampere's law are covered. Multipole expansions and the solution of boundary-value problems by separation of variable, and the method of images are discussed. Prerequisites: Phys 191-192 or Phys 193-194 or Phys 197-198 and Math 217, or permission of the instructor.
Course Attributes: FA NSM; EN TU; EN SU; EN DU; BU SCI; AR NSM; AS NSM; AS AN; EN BME T2

Instructors

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