VS265: Neural Computation: Difference between revisions
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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 | 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: 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 | ||
Brian Cheung, Mayur Mudigonda, GSI's | |||
* Email: | * Email: bcheung, mudigonda AT berkeley DOT edu (respectively) | ||
* Office: 567 Evans | * Office: 567 Evans | ||
* Office hours: | * Office hours: TBD | ||
=== Lectures === | === Lectures === | ||
* '''Location''': 560 Evans (Redwood Center Conference Hall) | * '''Location''': 560 Evans (Redwood Center Conference Hall) | ||
* '''Times''': | * '''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} == { | * '''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]] == | ||
== [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
- 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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