|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
0.00 - 4.00 Credits
This lab course complements NEU 502A and introduces students to the variety of techniques and concepts used in modern neuroscience, from the point of view of experimental and computational/quantitative approaches. Topics include electrophysiological recording, functional magnetic resonance imaging, psychophysics, and computational modeling. In-lab lectures give students the background necessary to understand the scientific content of the labs, but the emphasis is on the labs themselves. Second half of a double-credit core course required of all Neuroscience Ph.D. students.
-
0.00 - 4.00 Credits
Advanced seminar that reflects current research on brain and behavior.
-
0.00 - 4.00 Credits
An Introduction to the biophysics of nerve cells and synapses, the mathematical description of neural networks, and how neurons represent information. Course will survey computational modeling and data analysis methods for neuroscience and will parallel some topics from 549, but from a computational perspective. Topics will include representation of visual informaion, spatial navigation, short-term memory, and decision-making. Two 90 minute lectures, one laboratory. Lectures in common with MOL 437. Graduate students will carry out and write up an in-depth semester-long project. Prerequisite: 410, or elementary knowledge of linear algebra, di
-
0.00 - 4.00 Credits
This course serves as an introduction to nuclear magnetic resonance signal generation and magnetic resonance imaging (MRI) principles. Some common imaging techniques and their popular applications are fully described. The additional lab works are designed to provide opportunities to practice pulse sequence design and image reconstruction skills. The overall goal is to present an overview on state of the art MRI.
-
0.00 - 4.00 Credits
To acquaint the student with the language, mathematics and applications of probability and statistics in engineering and the sciences.
-
0.00 - 4.00 Credits
Optimization of deterministic systems, focusing on linear programming. Model formulations, the simplex method, sensitivity analysis, duality theory, network models, nonlinear programming. Applications to a variety of problems in optimal allocation of resources, transportation systems, and finance.
-
0.00 - 4.00 Credits
An introduction to probability and its applications. Random variables, expectation, independence. Poisson processes, Markov chains, and Brownian motion. Stochastic models of queues, population dynamics, and reliability.
-
0.00 - 4.00 Credits
A survey of quantitative approaches for making optimal decisions under uncertainty, including decision trees, Monte Carlo simulation, and stochastic programs. Forecasting and planning systems are integrated with a focus on financial applications.
-
0.00 - 4.00 Credits
This course introduces the pricing and hedging of options and other derivative securities. The main focus is on the binomial tree and the Black-Scholes models for equity markets. Other topics include credit risk, energy markets and stochastic volatility
-
0.00 - 4.00 Credits
Independent research or investigation resulting in a report in the student's area of interest under the supervision of a faculty member.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|