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CSE 371 Design of Digital Circuits and Systems (5)
Provides a theoretical background in, and practical experience with, tools, and techniques for modeling complex digital systems with the Verilog hardware description language, maintaining signal integrity, managing power consumption, and ensuring robust intra- and inter-system communication. Prerequisite: either E E 205 or E E 215; either E E 271 or CSE 369. Offered: jointly with E E 371.
View course details in MyPlan: CSE 371
Other software that way be useful for implementing Gaussian process models:
package by Ian
Nabney includes code for Gaussian process regression and many
other useful thing, . optimisers.
See Tom Minka 's
page on accelerating
matlab and his lightspeed
Seeger shares his code
for Kernel Multiple Logistic Regression, Incomplete Cholesky
Factorization and Low-rank Updates of Cholesky Factorizations.
See the software section of - .
Below is a collection of papers relevant to learning in Gaussian process
models. The papers are ordered according to topic, with occational papers
occuring under multiple headings.
| Covariance Functions
| Model Selection
| Learning Curves
| Reinforcement Learning
| Other Topics
Several papers provide tutorial material suitable for a first introduction to
learning in Gaussian process models. These range from very short [ Williams 2002 ] over intermediate [ MacKay 1998 ], [ Williams 1999 ]
to the more elaborate [ Rasmussen and Williams
2006 ]. All of these require only a minimum of prerequisites in the form of
elementary probability theory and linear algebra.
D. J. C. MacKay.
Theory, Inference and Learning Algorithms .
Cambridge University Press, Cambridge, UK, 2003.
chapter 45 .
Comment: A short introduction to GPs, emphasizing the
relationships to paramteric models (RBF networks, neural networks,