RW001 - Revision history

Chillar: /* Lectures */

2013-04-04T01:36:10Z

Lectures

← Older revision		Revision as of 01:28, 4 April 2013
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	=== Lectures ===		=== Lectures ===
			* NO CLASS April 4, 2013 !!!
	* '''Location''': 560 Evans (Redwood Center Conference Hall)		* '''Location''': 560 Evans (Redwood Center Conference Hall)
	* '''Times''': First and Third Thursdays - 3:30 PM to 5 PM		* '''Times''': First and Third Thursdays - 3:30 PM to 5 PM
	* Lecture 1 (intro lecture): [http://www.msri.org/people/members/chillar/files/Day1.pdf]		* Lecture 1 (intro lecture): [http://www.msri.org/people/members/chillar/files/Day1.pdf]
	* Lecture 2: [http://www.msri.org/people/members/chillar/files/Day2.pdf]		* Lecture 2: [http://www.msri.org/people/members/chillar/files/Day2.pdf]
			* Lectures 3,4: [http://www.msri.org/people/members/chillar/files/Day4.pdf]

	=== Enrollment information ===		=== Enrollment information ===

Chillar: /* Course description */

2013-02-25T21:26:50Z

Course description

← Older revision		Revision as of 21:18, 25 February 2013
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	Topics will attempt to cover (in no particular order and with no guarantees of them happening):		Topics will attempt to cover (in no particular order and with no guarantees of them happening):

	basic notions of matrix theory - vector spaces, rank, dimension, determinants, etc.		*basic notions of matrix theory - vector spaces, rank, dimension, determinants, etc.
			*eigenvectors and eigenvalues, norms and inequalities, matrix functions
	eigenvectors and eigenvalues, norms and inequalities, matrix functions		*special matrices - stochastic, orthogonal, unitary, symmetric, positive definite, etc.
			*matrix decompositions - SVD, polar, diagonalizability, Jordan form, etc.
	special matrices - stochastic, orthogonal, unitary, symmetric, positive definite, etc.		*applications to probability & data analysis - Max/Min Entropy, Markov chains, PCA, ICA, etc.
			*applications to optimization - Newton and Hessian methods, conjugate gradient, natural gradient etc.
	matrix decompositions - SVD, polar, diagonalizability, Jordan form, etc.		*applications to machine learning - spectral relaxations for clustering, gradients of objective functions in matrices, etc.
			*applications to dynamical systems - linear systems theory, associative networks, etc.
	applications to probability & data analysis - Max/Min Entropy, Markov chains, PCA, ICA, etc.		*applications to neuroscience - ACS, MPF, spike-timing max entropy neural codes, etc.
			*applications to noneq stat mech - Schnakenberg theory, Crooks & Jarzynski theory, etc.
	applications to optimization - Newton and Hessian methods, conjugate gradient, natural gradient etc.

	applications to machine learning - spectral relaxations for clustering, gradients of objective functions in matrices, etc.

	applications to dynamical systems - linear systems theory, associative networks, etc.

	applications to neuroscience - ACS, MPF, spike-timing max entropy neural codes, etc.

	applications to noneq stat mech - Schnakenberg theory, Crooks & Jarzynski theory, etc.

	=== Instructors ===		=== Instructors ===

Chillar: /* Course description */

2013-02-25T21:26:22Z

Course description

← Older revision		Revision as of 21:18, 25 February 2013
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	basic notions of matrix theory - vector spaces, rank, dimension, determinants, etc.		basic notions of matrix theory - vector spaces, rank, dimension, determinants, etc.

	eigenvectors and eigenvalues, norms and inequalities, matrix functions		eigenvectors and eigenvalues, norms and inequalities, matrix functions

	special matrices - stochastic, orthogonal, unitary, symmetric, positive definite, etc.		special matrices - stochastic, orthogonal, unitary, symmetric, positive definite, etc.

	matrix decompositions - SVD, polar, diagonalizability, Jordan form, etc.		matrix decompositions - SVD, polar, diagonalizability, Jordan form, etc.

	applications to probability & data analysis - Max/Min Entropy, Markov chains, PCA, ICA, etc.		applications to probability & data analysis - Max/Min Entropy, Markov chains, PCA, ICA, etc.

	applications to optimization - Newton and Hessian methods, conjugate gradient, natural gradient etc.		applications to optimization - Newton and Hessian methods, conjugate gradient, natural gradient etc.

	applications to machine learning - spectral relaxations for clustering, gradients of objective functions in matrices, etc.		applications to machine learning - spectral relaxations for clustering, gradients of objective functions in matrices, etc.

	applications to dynamical systems - linear systems theory, associative networks, etc.		applications to dynamical systems - linear systems theory, associative networks, etc.

	applications to neuroscience - ACS, MPF, spike-timing max entropy neural codes, etc.		applications to neuroscience - ACS, MPF, spike-timing max entropy neural codes, etc.

	applications to noneq stat mech - Schnakenberg theory, Crooks & Jarzynski theory, etc.		applications to noneq stat mech - Schnakenberg theory, Crooks & Jarzynski theory, etc.

Chillar: /* Course description */

2013-02-25T21:26:05Z

Course description

← Older revision		Revision as of 21:18, 25 February 2013
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	This is the first ever course of its kind on Matrix Analysis. Matrices are generally awesome and since most of us use Matlab, it would help to know more about them.		This is the first ever course of its kind on Matrix Analysis. Matrices are generally awesome and since most of us use Matlab, it would help to know more about them.

			The intention is to provide people some matrix theory and tools that are relevant for machine learning, artificial intelligence, and neuroscience.

			Topics will attempt to cover (in no particular order and with no guarantees of them happening):

			basic notions of matrix theory - vector spaces, rank, dimension, determinants, etc.
			eigenvectors and eigenvalues, norms and inequalities, matrix functions
			special matrices - stochastic, orthogonal, unitary, symmetric, positive definite, etc.
			matrix decompositions - SVD, polar, diagonalizability, Jordan form, etc.
			applications to probability & data analysis - Max/Min Entropy, Markov chains, PCA, ICA, etc.
			applications to optimization - Newton and Hessian methods, conjugate gradient, natural gradient etc.
			applications to machine learning - spectral relaxations for clustering, gradients of objective functions in matrices, etc.
			applications to dynamical systems - linear systems theory, associative networks, etc.
			applications to neuroscience - ACS, MPF, spike-timing max entropy neural codes, etc.
			applications to noneq stat mech - Schnakenberg theory, Crooks & Jarzynski theory, etc.

	=== Instructors ===		=== Instructors ===

Chillar: /* Lectures */

2013-02-25T19:04:43Z

Lectures

← Older revision		Revision as of 18:56, 25 February 2013
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	* '''Times''': First and Third Thursdays - 3:30 PM to 5 PM		* '''Times''': First and Third Thursdays - 3:30 PM to 5 PM
	* Lecture 1 (intro lecture): [http://www.msri.org/people/members/chillar/files/Day1.pdf]		* Lecture 1 (intro lecture): [http://www.msri.org/people/members/chillar/files/Day1.pdf]
			* Lecture 2: [http://www.msri.org/people/members/chillar/files/Day2.pdf]

	=== Enrollment information ===		=== Enrollment information ===

Chillar: /* Lectures */

2013-02-20T09:42:34Z

Lectures

← Older revision		Revision as of 09:34, 20 February 2013
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	* '''Location''': 560 Evans (Redwood Center Conference Hall)		* '''Location''': 560 Evans (Redwood Center Conference Hall)
	* '''Times''': First and Third Thursdays - 3:30 PM to 5 PM		* '''Times''': First and Third Thursdays - 3:30 PM to 5 PM
	* Lecture 1: [http://www.msri.org/people/members/chillar/files/Day1.pdf]		* Lecture 1 (intro lecture): [http://www.msri.org/people/members/chillar/files/Day1.pdf]

	=== Enrollment information ===		=== Enrollment information ===

Chillar: /* Lectures */

2013-02-20T09:41:51Z

Lectures

← Older revision		Revision as of 09:33, 20 February 2013
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	* '''Location''': 560 Evans (Redwood Center Conference Hall)		* '''Location''': 560 Evans (Redwood Center Conference Hall)
	* '''Times''': First and Third Thursdays - 3:30 PM to 5 PM		* '''Times''': First and Third Thursdays - 3:30 PM to 5 PM
			* Lecture 1: [http://www.msri.org/people/members/chillar/files/Day1.pdf]

	=== Enrollment information ===		=== Enrollment information ===

Mudigonda at 08:46, 20 February 2013

2013-02-20T08:46:11Z

← Older revision		Revision as of 08:38, 20 February 2013
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	This is the first ever course of its kind on Matrix Analysis. Matrices are generally awesome and since most of us use Matlab, it would help to know more about them.		This is the first ever course of its kind on Matrix Analysis. Matrices are generally awesome and since most of us use Matlab, it would help to know more about them.

	~~References:~~
	~~Matrix Analysis, Horn and Johnson.~~
	~~[http://www.maths.gla.ac.uk/~ajb/dvi-ps/2w-notes.pdf Basic Linear Algebra Notes]~~

	=== Instructors ===		=== Instructors ===
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	* Office: 567 Evans		* Office: 567 Evans

	[http://redwood.berkeley.edu/mayur Mayur Mudigonda]		[http://redwood.berkeley.edu/mayur Mayur Mudigonda] Web stuff

	=== Lectures ===		=== Lectures ===
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	=== Enrollment information ===		=== Enrollment information ===

			Open to all interested members of the Berkeley community. The lectures will not be recorded, so attending the fortnightly talks and e-mails are the only interactions.

	=== Email list and forum ===		=== Email list and forum ===

	=== Grading ===		=== Grading ===

			Self-grading with solutions in class. Since this is the first time this class is being offered, the details are still under work.

	=== 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.		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 ===		=== Textbooks ===

			Matrix Analysis, Horn and Johnson.
			[http://www.maths.gla.ac.uk/~ajb/dvi-ps/2w-notes.pdf Basic Linear Algebra Notes]

Chillar: /* Course description */

2013-02-15T05:21:56Z

Course description

← Older revision		Revision as of 05:13, 15 February 2013
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	This is the first ever course of its kind on Matrix Analysis. Matrices are generally awesome and since most of us use Matlab, it would help to know more about them.		This is the first ever course of its kind on Matrix Analysis. Matrices are generally awesome and since most of us use Matlab, it would help to know more about them.

	~~Also blah blah blah~~ and ~~blah!~~		References:
			Matrix Analysis, Horn and Johnson.
			[http://www.maths.gla.ac.uk/~ajb/dvi-ps/2w-notes.pdf Basic Linear Algebra Notes]

	=== Instructors ===		=== Instructors ===

Chillar: /* Instructors */

2013-02-15T05:20:36Z

Instructors

← Older revision		Revision as of 05:12, 15 February 2013
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	[Chris Hillar]		[Chris Hillar]
	* [http://www.msri.org/people/members/chillar/]		* [http://www.msri.org/people/members/chillar/ Website]
	* Office: 573 Evans		* Office: 573 Evans
	* Office hours: immediately following lecture or through e-mail		* Office hours: immediately following lecture or through e-mail