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Difference between revisions of "VS265: Neural Computation"

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(Created page with "This is the Fall 2014 VS 265 Neural Computation course webpage. == Course description == This course provides an introduction to the theory of neural computation. The goal ...")
 
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== Course description ==
 
== Course description ==
This course provides an introduction to the theory of neural computation.  The goal is to familiarize students with the major theoretical frameworks and models used in neuroscience and psychology, and to provide hands-on experience in using these models.  Topics include neural network models, supervised and unsupervised learning rules, associative memory models, probabilistic/graphical models, sensorimotor loops, and models of neural coding in the brain.
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This course provides an introduction to the theory of neural computation.  The goal is to familiarize students with the major theoretical frameworks and models used in neuroscience and psychology, and to provide hands-on experience in using these models.  Topics include neural network models, supervised and unsupervised learning rules, associative memory models, recurrent networks, probabilistic/graphical models, and models of neural coding in the brain.
  
This course differs from MCB 262, ''Advanced Topics in Systems Neuroscience,'' in that it emphasizes the theoretical underpinnings of models - i.e., their mathematical and computational properties - rather than their application to the analysis of neuroscientific data.  It is offered in alternate years, interleaving with MCB 262.  Students interested in computational neuroscience are encouraged to take both of these courses as they complement each other. This course was previously taught as [[VS298: Neural Computation|VS298]] ([[VS298 (Fall 06): Neural Computation|Fall 2006]], [[VS298: Neural Computation|Fall 2008]]) and [[VS265: Neural Computation Fall2010]].
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This course was previously taught as [[VS298: Neural Computation|VS298]] ([[VS298 (Fall 06): Neural Computation|Fall 2006]], [[VS298: Neural Computation|Fall 2008]]), [[VS265: Neural Computation Fall2010]], and [[VS265: Neural Computation Fall2012]].
  
 
=== Instructors ===
 
=== Instructors ===
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* Office hours: immediately following lecture
 
* Office hours: immediately following lecture
  
[http://redwood.berkeley.edu/wiki/mayur'''Mayur Mudigonda'''], GSI
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Brian Cheung, Mayur Mudigonda, GSI's
* Email: lastname AT berkeley DOT edu (from above)
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* Email: bcheung, mudigonda AT berkeley DOT edu (respectively)
 
* Office: 567 Evans
 
* Office: 567 Evans
* Office hours: 4-6 PM (Tu/Th) or by appointment
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* Office hours: TBD
  
 
=== Lectures ===
 
=== Lectures ===
 
* '''Location''': 560 Evans (Redwood Center Conference Hall)
 
* '''Location''': 560 Evans (Redwood Center Conference Hall)
* '''Times''': Mondays, Wednesdays - 9 to 10:30 AM  
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* '''Times''': Tuesday, Thursday - 3:30 to 5 PM  
 
* [http://www.archive.org/search.php?query=vs265%20berkeley '''Videos''']: graciously taped by our own from previous years [[Jeff Teeters]].
 
* [http://www.archive.org/search.php?query=vs265%20berkeley '''Videos''']: graciously taped by our own from previous years [[Jeff Teeters]].
  
 
=== Enrollment information ===
 
=== Enrollment information ===
 
* Open to both undergraduate and graduate students, subject to background requirements specified below.
 
* Open to both undergraduate and graduate students, subject to background requirements specified below.
* '''Telebears''': {CCN, Section, Units, Grade Option} == {66465, 01 LEC, 3, Letter Grade}
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* '''Telebears''': {CCN, Section, Units, Grade Option} == {66471, 01 LEC, 3, Letter Grade}
  
 
=== Email list and forum ===
 
=== Email list and forum ===
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== [[VS265: Homework assignments|Homework]] ==  
 
== [[VS265: Homework assignments|Homework]] ==  
 
== [[VS265: Class project|Class project]] ==
 
== [[VS265: Class project|Class project]] ==
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== [http://redwood.berkeley.edu/wiki/VS265:_Additional_resources Additional resources] ==
 
== [http://redwood.berkeley.edu/wiki/VS265:_Additional_resources Additional resources] ==
  
 
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Revision as of 18:29, 28 August 2014

This is the Fall 2014 VS 265 Neural Computation course webpage.

Course description

This course provides an introduction to the theory of neural computation. The goal is to familiarize students with the major theoretical frameworks and models used in neuroscience and psychology, and to provide hands-on experience in using these models. Topics include neural network models, supervised and unsupervised learning rules, associative memory models, recurrent networks, probabilistic/graphical models, and models of neural coding in the brain.

This course was previously taught as VS298 (Fall 2006, Fall 2008), VS265: Neural Computation Fall2010, and VS265: Neural Computation Fall2012.

Instructors

Bruno Olshausen

  • Email: link
  • Office: 570 Evans
  • Office hours: immediately following lecture

Brian Cheung, Mayur Mudigonda, GSI's

  • Email: bcheung, mudigonda AT berkeley DOT edu (respectively)
  • Office: 567 Evans
  • Office hours: TBD

Lectures

  • Location: 560 Evans (Redwood Center Conference Hall)
  • Times: Tuesday, Thursday - 3:30 to 5 PM
  • Videos: graciously taped by our own from previous years Jeff Teeters.

Enrollment information

  • Open to both undergraduate and graduate students, subject to background requirements specified below.
  • Telebears: {CCN, Section, Units, Grade Option} == {66471, 01 LEC, 3, Letter Grade}

Email list and forum

  • Please subscribe to the class email list here. The list name is vs265-students.

Grading

Based on weekly homework assignments (60%) and a final project (40%).

Required background

Prerequisites are calculus, ordinary differential equations, basic probability and statistics, and linear algebra. Familiarity with programming in a high level language such as Matlab is also required.

Textbooks

  • [HKP] Hertz, J. and Krogh, A. and Palmer, R.G. Introduction to the theory of neural computation. Amazon
  • [DJCM] MacKay, D.J.C. Information Theory, Inference and Learning Algorithms. Available online or Amazon
  • [DA] Dayan, P. and Abbott, L.F. Theoretical neuroscience: computational and mathematical modeling of neural systems. Amazon

HKP and DA are available as paperback. Additional reading, such as primary source material, will be suggested on a lecture by lecture basis.

Syllabus

Reading

Lecture slides

Homework

Class project

Additional resources

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