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Difference between revisions of "VS298: Subjectivity"

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(wiki page for VS298 Subjectivity (Fall 2017))
 
(Course description)
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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, recurrent networks, probabilistic/graphical models, and models of neural coding in the brain.
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This course is on subjectivity....blah blah blah
  
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]].
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This is a new course...
  
 
=== Instructors ===
 
=== Instructors ===
  
[http://redwood.berkeley.edu/bruno '''Bruno Olshausen''']
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[http://cornea.berkeley.edu '''Stan Klein''']
* Email: [http://mailhide.recaptcha.net/d?k=01XpDsm-AHoqUVpWDfs6Z2BQ==&c=LEppdYsjdtY8fds30WoF1cg5TLkIWqW94p40LB7hAwU= link]
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* Email: sklein@berkeley.edu
* Office: 570 Evans
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* Office: Minor
 
* Office hours: immediately following lecture
 
* Office hours: immediately following lecture
 
Brian Cheung, Mayur Mudigonda, GSI's
 
* Email: bcheung, mudigonda (respectively) AT berkeley DOT edu
 
* Office: 567 Evans
 
* Office hours: TBD
 
  
 
=== Lectures ===
 
=== Lectures ===
 
* '''Location''': 560 Evans (Redwood Center conference room)
 
* '''Location''': 560 Evans (Redwood Center conference room)
* '''Times''': Tuesday, Thursday - 3:30 to 5 PM 
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* '''Times''': Thursday - 2:00
* [http://www.archive.org/search.php?query=vs265%20berkeley '''Videos''']: graciously taped by our own from previous years [[Jeff Teeters]].
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=== 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} == {66471, 01 LEC, 3, Letter Grade}
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* '''Telebears''': {CCN, Section, Units, Grade Option} == {xx, xx, xx, xx}
  
 
=== Email list and forum ===
 
=== Email list and forum ===
* Please subscribe to the class email list [http://lists.berkeley.edu here]. The list name is vs265-students.
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* Please subscribe to the class email list [http://lists.berkeley.edu here]. The list name is xxx.
<!-- * A bulletin board is provided [http://redwood.berkeley.edu/forum/index.php here] for discussion regarding lecture material, readings, and problem sets. Code required for signup will be distributed to the class email list. -->
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=== Grading ===
 
=== Grading ===
Based on weekly homework assignments (60%) and a final project (40%).
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Based on....
  
 
=== Required background===
 
=== 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.
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Prerequisites are ...
 
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== Reading ==
 
== Reading ==

Revision as of 18:32, 17 August 2017

Course description

This course is on subjectivity....blah blah blah

This is a new course...

Instructors

Stan Klein

  • Email: sklein@berkeley.edu
  • Office: Minor
  • Office hours: immediately following lecture

Lectures

  • Location: 560 Evans (Redwood Center conference room)
  • Times: Thursday - 2:00

Enrollment information

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

Email list and forum

  • Please subscribe to the class email list here. The list name is xxx.

Grading

Based on....

Required background

Prerequisites are ...

Reading

  • [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.

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