Courses

Displaying 1 - 27 of 27
Course Course TItle Units Instructor Description
PSTAT 207A Statistical Theory 4.0

Univariate and multivariate distribution theory; generating functions; inequalities in statistics; order statistics; estimation theory: likelihood, sufficiency, efficiency, maximum likelihood; testing hypotheses: likelihood ratio and score tests, power; confidence and prediction intervals; bayesian estimation and hypothesis testing; basic decision theory, linear regression; analysis of variance. Part of a three quarter sequence with 207B and 207C.

PSTAT 207B Statistical Theory 4.0

Univariate and multivariate distribution theory; generating functions; inequalities in statistics; order statistics; estimation theory: likelihood, sufficiency, efficiency, maximum likelihood; testing hypotheses: likelihood ratio and score tests, power; confidence and prediction intervals; bayesian estimation and hypothesis testing; basic decision theory, linear regression; analysis of variance.  Part of a three quarter sequence with 207A and 207C.

PSTAT 207C Statistical Theory 4.0

Univariate and multivariate distribution theory; generating functions; inequalities in statistics; order statistics; estimation theory; likelihood, sufficiency, efficiency, maximum likelihood; testing hypotheses: likelihood ratio and score tests, power; confidence and prediction intervals; bayesian estimation and hypothesis testing; basic decision theory, linear regression; analysis of variance. Part of a three quarter sequence with 207A and 207B.

PSTAT 213A Introduction To Probability Theory And Stochastic Processes 4.0

Generating functions, discrete and continuous time Markov chains; random walks; branching processes; birth-death processes; Poisson processes, point processes.

 

PSTAT 213B Introduction to Probability Theory and Stochastic Processes 4.0

Convergence of random variables: different types of convergence; characteristic functions, continuity theorem, laws of large numbers, central limit theorem, large deviations, infinitely divisible and stable distributions, uniform integrability. Conditional expectation.

 

PSTAT 213C Introduction To Probability Theory And Stochastic Processes 4.0

Martingales, martingale convergence, stopping times, optional sampling, optional stopping theorems and applications, maximal inequalities. Brownian motion, introduction to diffusions.

 

Math 214A Ordinary Differential Equations 4.0

Existence, uniqueness, and stability; the geometry of phase space; linear systems and hyperbolicity; maps and diffeomorphisms.

 

Math 214B Chaotic Dynamics and Bifurcation Theory 4.0

Hyberbolic structure and chaos; center manifolds; bifurcation theory; and the Feigenbaum and Ruelle-Takens cascades to strange attractors.

 

ME 215A Applied Dynamical Systems I 3.0

Phase-plane methods, non-linear oscillators, stability of fixed points and periodic orbits, invariant manifolds, structural stability, normal form theory, local bifurcations for vector fields and maps, applications from engineering, physics, chemistry, and biology.

 

ME 215B Applied Dynamical Systems II 3.0

Local codimension two bifurcations, global bifurcations, chaos for vector fields and maps, Smale horshoe, symbolic dynamics, strange attractors, universality, bifyrcation with symmetry, perturbation theory and averaging, Melnikov's method, canards, applications from engineering, physics, chemistry, and biology.

 

PHYS 219 Statistical Mechanics 4.0

Fundamental principles of classical and quantum statistics. Non-interacting Boltzmann, Bose, and Fermi systems. Virial expansion and other approaches to interacting systems. Phase transitions.

 

PSY 221E Statistical Analysis of fMRI Data 4.0

Experimental design and statistical analysis in fMRI research. Linear and nonlinear models of the hemodynamic response function, the general linear model in fMRI analysis, post hoc testing, group testing with the random effects model, and connectivity analysis.

 

PHYS 223C Concepts and Phenomena of Condensed Matter Physics 4.0

Lattice and electron dynamics. Elementary excitations and collective phenomena. Transport properties. Disorder and localization. Long-range order and broken symmetries. Magnetism, superconductivity and liquid crystals. Properties and structures of polymers, membranes, and self-assembling systems.

 

CMPSC 225 / ECE 205A Information Theory 4.0

Entropy, mutual information, and Shannon's coding theorems; lossless source coding, Huffman, Shannon-Fano-Elias, and arithmetic codes; Channel capacity; rate-distortion theory, and lossy source coding; source-channel coding; algorithmic complexity and information; applications of information theory in various fields.

 

PSY 228 Perception 4.0

Analysis of psychophysical relations in sensory processes with stress on detection, scaling, discrimination, spatial and temporal resolution, and the interaction of cue systems in perceptual behavior.

 

ECE 230A/ME 243A Linear Systems I 4.0

Internal and external descriptions. Solution of state equations. Controllability and observability realizations. Pole assignment, observers;modern compensator design. Disturbance localization and decoupling. Least-squares control. Least-squares estimation; kalman filters; smoothing,the separation theorem; LQG compensator design. Computational considerations. Selected additional topics.

PSY 231 Cognitive Neuroscience 4.0

Examination of the neurological basis of cognition with material drawn fromresearch in psychology, neurology and the neurosciences with brain injured and healthy human and non-human subjects. Topics include memory, language, and perception.

 

PSY 232 Neuroimaging 4.0

Introduces students to the theoretical and practical issues involved in conducting functional magnetic resonance imaging (fMRI) experiments. Content includes basic MR physics, physiology of the BOLD signal, experimental design, image processing, statistical analysis, and brain mapping.

 

CMPSC 234 Randomized Algorithms 4.0
ECE 235 Stochastic Processes in Engineering 4.0

A first-year graduate course in Stochas TIC processes, including: review of basic probability; gaussian, poisson, and Weiner processes; wide-sense stationary processes; covariance function and power spectral density; linear systems driven by random inputs; basic Wiener and Kalman filter theory.

ECE 236 Nonlinear Control Systems 4.0

Analysis and design of nonlinear control systems. Focus on Lyapunov stability theory, with sufficient time devoted to contrasts between linear and nonlinear systems, input-output stability and the describing function method.

 

ME 243B/ ECE 230B Linear Systems II 4.0

Internal and external descriptions. Solution of state equations. Controllability and observability realizations. Pole assignment, assignment, observers; modern compensator design. Distribance localization and decoupling. Least-squares control. Least-squares estimation; Kalman filters; smoothing. The seperation theorem; LQG compensator design. Computational considerations. Selected additional topics.

 

MCDB 251 Neurobiology I: Cellular Organization and Biophysics of the Nervous System 4.0

Nervous system properties ranging from single cells to whole organisms, using examples from vertebrates/invertebrates studied in terms of morphology, physiology, behavior.

(Note: PSY 269 Neuroanatomy maybe taken in place of this course)

 

PSY 265 Computational Neuroscience 4.0

Survey of methods in computational neuroscience; single cell methods including Hodgkin-Huxley models, occupation theory, integrate-and-fire models; neural network modeling including linear system theory, nonlinear dynamics, connectionism, Hodgkin-Huxley-like network models, models of synaptic plasticity, methods for generating predicted BOLD signals.

 

PSY 269 Neuroanatomy 4.0

An examination of the organization of the vertebrate nervous system. Topicsinclude neurohistological techniques; neurology and neuropsychology; comparative neuroanatomy; neural degeneration; developmental neuroscience.

(Note MCDB 251 Neuobiology may be taken in place of the course)

 

CMPSC 281B/ ECE 281B Advanced Topics in Computer Vision 4.0

Advanced topics in computer vision: image sequence analysis, spatio-temporal filtering, camera calibration and hand-eye coordination, robot navigation, shape representation, physically-based modeling, regularization theory, multi-sensory fusion, biological models, expert vision systems, and other topics selected from recent research papers.

 

DYNS 592 Special Interest Group Research Seminar 1

Research seminar in dynamical neuroscience.

 

CORE Course Requirements

Course Course TItle Units Instructor Description
PSY 265 Computational Neuroscience 4.0

Survey of methods in computational neuroscience; single cell methods including Hodgkin-Huxley models, occupation theory, integrate-and-fire models; neural network modeling including linear system theory, nonlinear dynamics, connectionism, Hodgkin-Huxley-like network models, models of synaptic plasticity, methods for generating predicted BOLD signals.

 

ECE 230A/ME 243A Linear Systems I 4.0

Internal and external descriptions. Solution of state equations. Controllability and observability realizations. Pole assignment, observers;modern compensator design. Disturbance localization and decoupling. Least-squares control. Least-squares estimation; kalman filters; smoothing,the separation theorem; LQG compensator design. Computational considerations. Selected additional topics.

Math 214A Ordinary Differential Equations 4.0

Existence, uniqueness, and stability; the geometry of phase space; linear systems and hyperbolicity; maps and diffeomorphisms.

 

MCDB 251 Neurobiology I: Cellular Organization and Biophysics of the Nervous System 4.0

Nervous system properties ranging from single cells to whole organisms, using examples from vertebrates/invertebrates studied in terms of morphology, physiology, behavior.

(Note: PSY 269 Neuroanatomy maybe taken in place of this course)

 

PSY 269 Neuroanatomy 4.0

An examination of the organization of the vertebrate nervous system. Topicsinclude neurohistological techniques; neurology and neuropsychology; comparative neuroanatomy; neural degeneration; developmental neuroscience.

(Note MCDB 251 Neuobiology may be taken in place of the course)