Common Course Numbers Courses offered at Penn State are identified by departmental abbreviation and number.
This is an advanced course on further topics in mathematical biology. Topic will vary according to the instructor. Possible topics include modeling infectious diseases, cancer modeling, mathematical neurosciences or biological oscillators, among others.
The sample description below is for a course in biological oscillators from Winter From sleeping patterns, heartbeats, locomotion, and firefly flashing to the treatment of cancer, diabetes, and neurological disorders, oscillations are of great importance in biology and medicine.
Mathematical modeling and analysis are needed 500 level courses undergraduate understand what causes these oscillations to emerge, properties of their period and amplitude, and how they synchronize to signals from other oscillators or from the external world.
Models will typically use ordinary differential equations. Mathematical techniques introduced in this course include 1 the method of averaging, 2 harmonic balance, 3 Fourier techniques, 4 entrainment and coupling of oscillators, 4 phase plane analysis, and 5 various techniques from the theory of dynamical systems.
Emphasis will be placed on primary sources papers from the literature particularly those in the biological sciences. Teamwork will be encouraged. Math - Combinatorics and Graph Theory Prerequisites: Math, or equivalent experience with abstract mathematics Credit: This course has two somewhat distinct halves devoted to 1 graph theory and 2 topics in the theory of finite partially ordered sets.
Students should have taken at least one proof-oriented course. The first part of this course will be devoted to graph theory. The second part of the course will deal with topics in the theory of finite partially ordered sets.
Math - Combinatorial Theory Mathor equivalent experience with abstract algebra Credit: This course is a rigorous introduction to classical combinatorial theory.
Concepts and proofs are the foundation, but there are copious applications to modern industrial problem-solving. Permutations, combinations, generating functions, and recurrence relations.
The existence and enumeration of finite discrete configurations. Construction of combinatorial designs. Math - Introduction to Coding Theory Prerequisites: Math, or Credit: This course is designed to introduce mathematics majors to an important area of applications in the communications industry.
Using linear algebra it will cover the foundations of the theory of error-correcting codes and prepare a student to take further EECS courses or gain employment in this area.
For EECS students it will provide a mathematical setting for their study of communications technology. Introduction to coding theory focusing on the mathematical background for error-correcting codes.
Further topics range from asymptotic parameters and bounds to a discussion of algebraic geometric codes in their simplest form. Math or for undergrad students or Graduate standing. In this course, students will derive, interpret and solve mathematical models of neural systems.
The neural models consist of ordinary and partial differential equations and students are required to analytically and numerically solve the equations. Additional mathematical analysis techniques of phase plane analysis, linear stability of equilibria and bifurcation analysis will also be covered.
Computational neuroscience provides a set of quantitative approaches to investigate the biophysical mechanisms and computational principles underlying the function of the nervous system. This course introduces students to mathematical modeling and quantitative techniques used to investigate neural systems at many different scales, from single neuron activity to the dynamics of large neuronal networks.
Math - Numerical Linear Algebra Prerequisites: Math, or and one of Math, or ; or permission of instructor Credit: This course is an introduction to numerical linear algebra, which is at the foundation of much of scientific computing.
Numerical linear algebra deals with 1 the solution of linear systems of equations, 2 computation of eigenvalues and eigenvectors, and 3 least squares problems.
We will study accurate, efficient, and stable algorithms for matrices that could be dense, or large and sparse, or even highly ill-conditioned.
The course will emphasize both theory and practical implementation.MECE E Fundamentals of engineering.
1 point.. Lect: 3. Prerequisites: Senior Standing. Review of core courses in mechanical engineering, including mechanics, strength of materials, fluid mechanics, thermodynamics, heat transfer, materials and processing, control, and mechanical design and analysis. Apply.
Before you apply, review your program application requirements.. START YOUR APPLICATION. Military and Veterans. If you’re entering a non-nursing undergraduate program: skip the application and enroll directly.
May 16, · Or are level courses for undergraduates who are fantastic in the subject matter and for graduate students. I will be a senior, GPA. My econ grades so far have been A, A- (his class),B+,B, and grupobittia.com: Resolved.
An undergraduate degree (also called first degree, bachelor's degree or simply degree) is a colloquial term for an academic degree taken by a person who has completed undergraduate courses. It is usually offered at an institution of higher education, such as a grupobittia.com most common type of this degree is the bachelor's degree, which .
Students should have significant experience in writing proofs at the level of Math and should have a basic understanding of groups, rings, and fields, at least at the level of Math and prefer- . Graduate courses at the , level are open to juniors, seniors and to sophomores who have declared their major.
Undeclared sophomores and very rarely freshmen require special permission. Graduate courses at the to level are not open to undergraduates, although exceptions are made on rare occasions.