SVM Rules of Thumb
Description
We provide some intuition into how to use SVMs. We give advice about
choosing the regularization parameter C, the kernel and its parameters, and
the size of the subprob
9.09J/7.29J - Cellular Neurobiology, Spring 2005
Massachusetts Institute of Technology
Department of Brain and Cognitive Sciences
Department of Biology
Instructors: Professors William Quinn and Troy L
9.09J/7.29J - Cellular Neurobiology, Spring 2005
Massachusetts Institute of Technology
Department of Brain and Cognitive Sciences
Department of Biology
Instructors: Professors William Quinn and Troy L
9.09J/7.29J - Cellular Neurobiology, Spring 2005
Massachusetts Institute of Technology
Department of Brain and Cognitive Sciences
Department of Biology
Instructors: Professors William Quinn and Troy L
A systems neuroscience
approach to memory
Critical brain structures for declarative memory
Relational memory vs. item memory
Recollection vs. familiarity
Recall vs. recognition
What about PDs?
R-K pa
Parkinsons disease and Memory
Christie Chung
8/2/06
PD and Memory
PD without dementia impairs declarative
memory processes
Recall
Recognition (Recollection and Familiarity)
Prospective mem
Paper Four: Revision
DUE TO YOUR TA BY 9AM ON FRI, DEC 3
If you write a newspaper story, a grant proposal, or a piece of prose for a textbook, it is very
likely that the first draft will not be the la
Paper Three: Rewriting the textbook
DUE TO YOUR TA BY 9AM ON FRI, NOV 5
Paper Three must be drawn from the materials relating to Lectures 17-24 / Chapters 13, 15-17
For this paper, you goal is to draf
What is Memory?
Memory properis the knowledge of a former state of mind after it has already once
dropped from consciousness; or rather it is the knowledge of an event, or fact, of which
meantime we h
Paper Two: Taking the next step
DUE TO YOUR TA BY 9AM ON FRI, OCT 8
Paper Two must be drawn from the materials relating to Lectures 7-16 / Chapters 8-12, & 14
For this paper, you goal is to identify a
Lecture 18: Thomas Serre
Supplementary reading list
T. Serre. Learning a dictionary of shape-components in visual cortex:
Comparison with neurons, humans and machines, PhD Thesis, May 2006
T. Serre, L
Paper One: Writing for the public
DUE TO YOUR TA BY 9AM ON FRI, SEPT 24
Topics: Note: You can write about a topic before you have heard the lecture on the topic
The Paper One topic must be drawn from
Lecture 09:
Sayan Mukherjee
Description
Necessary and sufficient requirements for uniform convergence for both real-valued loss
functions and classification are introduced. VC entropy, VC dimension, e
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24.08J / 9.48J Philosophical Issues in Brain Science
Spring 2009
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Your schedule of coming weeks
Today: One-way ANOVA, part II
Next week:
Two-way ANOVA, parts I and II.
One-way ANOVA HW due Thursday
Week of May 4
Teacher out of town all week
No class on Tuesday, 5
Lecture 16:
Alex Rakhlin
Description
We relate consistency or empirical risk minimization (ERM) with uniform convergence
in probability over function classes. We prove generalization bounds for ERM in
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Spring 2009
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Review
One-way ANOVA, I
9.07
4/15/2004
Multiple comparisons
We often need a tool for comparing more
than two sample means
Earlier in this class, we talked about twosample z- and t-tests for the diff
Lecture 15: Stability of Tikhonov
Regularization
Alex Rakhlin
Description
We briefly review the generalization bounds of last lecture before turning to our main
goal - using the stability approach to
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24.08J / 9.48J Philosophical Issues in Brain Science
Spring 2009
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Summary sheet from last time:
Confidence intervals
Confidence intervals take on the usual form: parameter =
statistic tcrit SE(statistic)
parameter
a
b
SE
se sqrt(1/N + mx2/ssxx)
se/sqrt(ssxx)
y (me
Lecture 14: Generalization Bounds and
Stability
Alex Rakhlin
Description
We introduce the notion of generalization bounds, which allow us to have confidence in
the functions our algorithms are finding
Outline Correlation & Regression, III
9.07 4/6/2004 Relationship between correlation and regression, along with notes on the correlation coefficient Effect size, and the meaning of r Other kinds
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24.941J / 6.543J / 9.587J / HST.727J The Lexicon and Its Features
Spring 2007
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Steps in regression analysis (so far)
Correlation & Regression, II
9.07
4/6/2004
Residual Plots
Plotting the residuals (yi yi) against xi can
reveal how well the linear equation explains
the data
Ca
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Spring 2009
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Regression and correlation
Involve bivariate, paired data, X & Y
Correlation & Regression, I
9.07
4/1/2004
Regression & correlation
Concerned with the questions:
Does a statistical relationship exi
Lecture 12: Online Learning
Andrea Caponnetto, Sanmay Das
Description
To introduce the general setting of online learning. To describe an online version of the
RLS algorithm and analyze its performanc
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24.08J / 9.48J Philosophical Issues in Brain Science
Spring 2009
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