Independent Component Analysis: Algorithms and Applications
Aapo Hyvrinen and Erkki Oja Neural Networks Research Centre Helsinki University of Technology P.O. Box 5400, FIN-02015 HUT, Finland Neural Networks, 13(4-5):411-430, 2000
Abstract A fundamental p
Introduction to Statistical Learning Theory
Olivier Bousquet1 , Stphane Boucheron2 , and Gbor Lugosi3
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1
2
3
Max-Planck Institute for Biological Cybernetics
Spemannstr. 38, D-72076 Tbingen, Germany
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[email protected]
http:/www.kyb.mpg.de/~bousq
1
A primer on kernel methods
Jean-Philippe Vert
Koji Tsuda
Bernhard Schlkopf
o
Kernel methods in general, and support vector machines (SVMs) in particular, are
increasingly used to solve various problems in computational biology. They oer
versatile tools
Lecture 7: More on Learning Theory. Introduction to
Active Learning
VC dimension
Denition of PAC learning
Motivation and examples for active learning
Active learning scenarios
Query heuristics
With thanks to Burr Settles, Sanjoy Dasgupta, John Langford fo
Lecture 6: Introduction to learning theory
True error of a hypothesis (classication)
Some simple bounds on error and sample size
Introduction to VC-dimension
COMP-652 and ECSE-608 (Instructor: Doina Precup), Lecture 6, January 22, 2015
1
Binary classic
http:/mcgill.oxdia.com
Midterm Exam
Fundamentals of Computer Vision
COMP 558
Oct. 13, 2010
Prof. M. Langer
There are a total of 11 points (10 + 1 bonus on Question 4).
1. (1 point)
Consider a thin lens camera with f-number N = 4 and focal length 30 mm. Su
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STUDENT NAME:
STUDENT ID:
MIDTERM EXAMINATION
Machine Learning - Fall 2006
November 1, 2006
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You are allowed one double-sided cheat sheet
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Read all the questions before you start working. Please write your answer on the
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STUDENT NAME:
STUDENT ID:
MIDTERM EXAMINATION
Machine Learning - Fall 2005
October 27, 2005
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You are allowed one double-sided cheat sheet
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Read all the questions before you start working. Please write your answer on the
COMP 652: Machine Learning - Assignment 1
Posted Wednesday, September 9, 2009
Due Wednesday, September 16, 2009
1. Linear and polynomial regression [65 points]
For this exercise, you will experiment in Matlab with linear and polynomial regression on a giv
COMP 652 Final Project By Jules Fakhoury December 15, 2010
12/15/2010
1
High fluctuation and irregularity in the data
Artificial Neural Network (ANN)
Outperforms most statistical methods Based on the empirical risk minimization which minimizes the error
Lecture 4: More on neural networks
Finding a good network structure: overtting Automatic construction of network structure:
Constructive methods Destructive methods
Other kinds of networks
Autoencoders Recurrent neural networks
September 17
Lecture 3: Feed-forward neural networks. Backpropagation
Network architecture Backpropagation algorithm Tweaks:
Avoiding local minima Choosing learning rates Encoding the inputs and outputs
September 12, 2007
1
COMP-652 Lecture 3
The need
Lecture 2: Classication. Perceptron. Sigmoid classiers.
Classication problems. Error functions Perceptron Sigmoid classiers
September 10, 2007
1
COMP-652 Lecture 2
Classication
Given a data set D X Y where Y is a discrete set (usually
wit
The Expectatio
Maximization Algorithm
common task in signal processing is the estimation
of the parameters of a probability distribution func.tion. Perhaps the most frequently encountered estimation problem is the estimation of the mean of a signal in
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