University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Spring 1996
Midterm Exam
(one page of notes allowed)
100 points, 90 minutes April 29, 1996
Write your answers on these pages and show your work. If you feel that a questi
University of Wisconsin Madison
Computer Sciences Department
CS 760 - Machine Learning
Spring 2010
Exam
11am-12:30pm, Monday, April 26, 2010
Room 1240 CS
CLOSED BOOK
(one sheet of notes and a calculator allowed)
Write your answers on these pages and show
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Spring 1989
Midterm Exam (Open Book)
100 points, 90 minutes April 27, 1989
Write your answers on these pages and show your work. If you feel that a question is not fully
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Spring 1992
Midterm Exam
(one page of notes allowed)
100 points, 90 minutes April 28, 1992
Write your answers on these pages and show your work. If you feel that a questi
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Spring 1993
Midterm Exam
(calculator and one page of notes allowed)
100 points, 90 minutes April 19, 1993
Write your answers on these pages and show your work. If you fee
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Spring 1990
Midterm Exam
(ve pages of notes allowed)
100 points, 90 minutes May 1, 1990
Write your answers on these pages and show your work. If you feel that a question
University of Wisconsin Madison
Computer Sciences Department
CS 760 - Machine Learning
Spring 2003
Exam
7:15-9:15pm, May 6, 2003 Room 3345 Engineering Hall CLOSED BOOK (one sheet of notes and a calculator allowed)
Write your answers on these pages and sho
CHAPTER 1
GENERATIVE AND DISCRIMINATIVE
CLASSIFIERS:
NAIVE BAYES AND LOGISTIC REGRESSION
Machine Learning
Copyright c 2005, 2010. Tom M. Mitchell. All rights reserved.
*DRAFT OF January 19, 2010*
*PLEASE DO NOT DISTRIBUTE WITHOUT AUTHORS
PERMISSION*
This
Support Vector and Kernel Machines
Nello Cristianini BIOwulf Technologies nello@support-vector.net http:/www.support-vector.net/tutorial.html
ICML 2001
A Little History
z
z
z
z
SVMs introduced in COLT-92 by Boser, Guyon, Vapnik. Greatly developed ever sin
c 2016 Robert Nowak
Note on Cross-Validation
Let f be the minimizer of the regularized problem
(
)
N
1 X
min
L(yi , f (xi ) + c(f ) ,
f F
N i=1
(1)
where F is a class of predictors, L is a loss function (e.g., squared error, logistic loss, hinge loss, etc
c 2016 Robert Nowak
Note on Lasso
This is a short note is based on the analysis framework developed in [1]. Let w? be a s-sparse vector and
suppose that we observe
y = Xw? + ,
iid
where X is a known n p matrix with entries Xij N (0, 1) and is an unknown e
c 2016 Robert Nowak
Note on Proximal Gradient Algorithms
These notes consider optimization problems of the following form
min f (w) + c(w) ,
wRp
where the functions f and c are convex, and f is also differentiable. Special cases include ridge regression
a
2016 Rebecca Willett
Backpropagation in Neural Networks
Artificial neural networks can be used to learn predictors in a wide variety of machine learning settings.
The basic idea is take a feature vector x Rp , compute different weighted combinations of t
2016 Rebecca Willett
Stochastic Gradient Descent
In many machine learning and signal processing settings, we wish to solve an optimization problem of
the form
minimize f (w)
w
where the objective function can be decomposed as
f (w) =
n
X
fi (w).
i=1
For
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Spring 1989
Midterm Exam
100 points March 24, 1988
Neatly answer the following questions in the space provided. If you feel a question is ambiguous, clearly state any ass
University of Wisconsin Madison
Computer Sciences Department
CS 760 - Machine Learning
Fall 2006
Exam
1-2:30pm, December 8, 2006
Room 1325 Computer Sciences
CLOSED BOOK
(one sheet of notes and a calculator allowed)
Write your answers on these pages and sh
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Spring 1994
Midterm Exam
(calculator and one page of notes allowed)
100 points, 90 minutes April 25, 1994
Write your answers on these pages and show your work. If you fee
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Spring 1995
Midterm Exam
(two pages of notes allowed)
100 points, 90 minutes May 3, 1995
Write your answers on these pages and show your work. If you feel that a question
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Fall 1997
Midterm Exam
(one page of notes allowed)
100 points, 90 minutes December 3, 1997
Write your answers on these pages and show your work. If you feel that a questi
University of Wisconsin-Madison
Computer Sciences Department
CS 760 Machine Learning
Fall 1998
Exam
(one page of notes and calculators allowed)
100 points, 105 minutes December 10, 1998
Write your answers on these pages and show your work. If you feel tha
Solution to CS760 HW 4 (Spring 2010)
1. Initial values: all Q=3.
L
Start
Q=3
R
a
R Q=3
b
L
Q=3
C
Q=3
C
L
Q=3
Q=3
Q=3
d
R
Q=3
end
C
Q=3
i) For the first episode: start->a->b->d->end, we have the following Q values:
Step 1: start->a. We have Q ( start , L)
CS 760 - Homework 4
Out: 4/12/10
Due: 4/19/10
50 points
Consider the deterministic reinforcement environment drawn below. The numbers on the arcs
are the immediate rewards. Let the discount rate equal 0.8 and the probability of taking an
exploration step
University of Wisconsin Madison
Computer Sciences Department
CS 760 - Machine Learning
Fall 1999
Exam
7:15-9:15pm, December 14, 1999
Room 1240 CS & Stats
CLOSED BOOK
(one sheet of notes and a calculator allowed)
Write your answers on these pages and show
University of Wisconsin Madison
Computer Sciences Department
CS 760 - Machine Learning
Fall 2001
Exam
7:15-9:15pm, December 13, 2001
Room 1240 CS & Stats
CLOSED BOOK
(one sheet of notes and a calculator allowed)
Write your answers on these pages and show
University of Wisconsin Madison
Computer Sciences Department
CS 760 - Machine Learning
Spring 2001
Exam
7:15-9:15pm, May 2, 2001
Room 2317 Engineering Hall
CLOSED BOOK
(one sheet of notes and a calculator allowed)
Write your answers on these pages and sho
University of Wisconsin Madison
Computer Sciences Department
CS 760 - Machine Learning
Fall 2004
Exam
11am-12:45pm, December 10, 2004 Room 107 Psychology CLOSED BOOK (one sheet of notes and a calculator allowed)
Write your answers on these pages and show
University of Wisconsin Madison
Computer Sciences Department
CS 760 - Machine Learning
Spring 2009
Exam
7:15-9:15pm, Wednesday, April 29, 2009
Room 1240 CS
CLOSED BOOK
(one sheet of notes and a calculator allowed)
Write your answers on these pages and sho
Journal of Machine Learning Research 11 (2010) 61-87
Submitted 11/09; Published 1/10
Model Selection: Beyond the Bayesian/Frequentist Divide
Isabelle Guyon
GUYON @ CLOPINET. COM
ClopiNet
955 Creston Road
Berkeley, CA 94708, USA
Amir Saffari
SAFFARI @ ICG
The Case Against Accuracy Estimation for Comparing Induction Algorithms
Bell Atlantic Science and Tech 400 Westchester Avenue White Plains, NY 10604
foster@basit.com
Foster Provost
Bell Atlantic Science and Tech 400 Westchester Avenue White Plains, NY 106
c 2016 Robert Nowak
Rademacher Complexity and Learning with Convex Loss Functions
1
Convex Losses
Suppose we have training data cfw_xi , yi ni=1 , a set of prediction rules F, and a loss function L. Empirical risk
minimization is the optimization
n
1X
min