CS446: Machine Learning
Fall 2015
Problem Set 5
Handed Out: October 30th , 2015
Due: November 07th , 2015
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you dem
CS446: Machine Learning
Fall 2015
Problem Set 7
Handed Out: November 19th , 2015
Due: December 3rd , 2015
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you dem
CS446: Machine Learning
Fall 2015
Problem Set 1
Ocial Solution
Handed In: September 16, 2015
1. [Learning Conjunctions - 40 points]
a. The LearnConjunction algorithm (Algorithm 1) will begin with the most specic hypothesis, i.e. the complete conjunction o
Midterm exam on 10/23
(in class; closed books).
Projects
Next week: Kai-Wei Chang
- Will give a review
- SVM
Term papers/Projects proposals are due on Thursday, 10/09/14.
Within a week we will give you an approval to continue with your project
along with
CS446: Machine Learning
Fall 2013
Problem Set 4
Due: October 31th , 2013
Handed Out: October 17, 2013
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you demonst
CS446: Machine Learning
Fall 2014
Problem Set 7
Handed Out: November 18th , 2014
Due: December 3rd , 2014
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you dem
CS446: Machine Learning
Fall 2011
Mid-term Exam
October 25th , 2011
This is a closed book exam. Everything you need in order to solve the problems is
supplied in the body of this exam. Note that there is an appendix with possibly useful
formulae and comp
CS446: Machine Learning
Fall 2016
Problem Set 5
Due: November 8th , 2016
Handed Out: Official Solution
1. [PAC Learning - 40 points]
(a) One possible algorithm is the following: given data (xi , yi ) : i cfw_1, . . . , m),
return the largest radius of any
CS446: Pattern Recognition and Machine Learning
Fall 2008
Problem Set 1
Handed Out: September 2, 2008 Due: September 11, 2008
Feel free to talk to other members of the class in doing the homework. I am more concerned that you learn how to solve the probl
CS446: Machine Learning
Fall 2016
Problem Set 3 Solution
Handed Out: October 11, 2016
Due: N/A
Experiment 1: Number of examples vs Number of mistakes
Algorithm
Parameters
NA
Dataset
n=500
NA
0.005
Dataset
n=1000
NA
0.005
Perceptron
Perceptron
w/margin
Win
CS446: Machine Learning
Fall 2013
Problem Set 5
Handed Out: October 31th , 2013
Due: November 14th , 2013
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you dem
CS446: Machine Learning
Fall 2014
Problem Set 3
Handed Out: September 29th , 2014
Due: October 10th , 2014
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you de
CS446: Machine Learning
Fall 2005
Midterm Exam Solutions : Addendum
October 23, 2011
Problem 4. (d) [On-Line Learning and PAC Learning]
Explain what is required in order to guarantee ( , ) behavior (recall (b).
Solution:
In order to guarantee ( , ) behav
CS446: Machine Learning
Fall 2012
Mid-term Exam Solutions
October 30th , 2012
This is a closed book exam. Everything you need in order to solve the problems is
supplied in the body of this exam. Note that there is an appendix with possibly useful
formula
CS446: Machine Learning
Fall 2016
October 25th , 2016
This is a closed book exam. Everything you need in order to solve the problems is
supplied in the body of this exam.
This exam booklet contains four problems. You need to solve all problems to get
10
CS446: Machine Learning
Fall 2016
Problem Set 5
Handed Out: October 27th , 2016
Due: November 08th , 2016
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you dem
CS446: Machine Learning
Fall 2016
Problem Set 7
Handed Out: November 17th , 2016
Due: December 1st , 2016
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you dem
CS446: Machine Learning
Spring 2015
Problem Set 4
Handed Out: March 12, 2015
Due: April 2, 2015
Feel free to talk to other members of the class in doing the homework. I am more concerned that you learn how to solve
the problem than that you demonstrate t
CS446: Machine Learning
Spring 2015
Problem Set 3
Ocial Solution
Handed In: Mar. 12, 2015
In this solution, we present the experimental results conducted by the TA so that the students can
compare their results to the TAs results. Note that your homework
CS446: Machine Learning
Fall 2015
Problem Set 7
Handed Out: November 19th , 2015
Due: December 3rd , 2015
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you dem
CS446: Machine Learning
Spring 2015
Problem Set 5
Handed Out: April 2nd , 2015
Due: April 16th , 2015
1. [Boosting with AdaBoost - 30 points]
(a) The initial weight distribution is uniform, meaning all data points get a weight of
1
10
(b) If you try to an
CS446: Machine Learning
Fall 2011
Mid-term Exam Solution
October 25th , 2011
This is a closed book exam. Everything you need in order to solve the problems is
supplied in the body of this exam. Note that there is an appendix with possibly useful
formulae
CS446: Machine Learning
Fall 2012
Mid-term Exam
October 30th , 2012
This is a closed book exam. Everything you need in order to solve the problems is
supplied in the body of this exam. Note that there is an appendix with possibly useful
formulae and comp
CS446: Machine Learning
Fall 2013
Problem Set 1
Handed Out: September 5, 2013
Due: September 19, 2013
Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you demonst
CS446: Machine Learning
Fall 2014
Mid-term Exam Solutions
October 23rd , 2014
This is a closed book exam. Everything you need in order to solve the problems is
supplied in the body of this exam.
This exam booklet contains four problems. You need to solv
Why does it work?
We have not addressed the question of why
does this classifier performs well, given that the
assumptions are unlikely to be satisfied.
The linear form of the classifiers provides some
hints.
Bayesian Learning
CS446 -FALL 14
Nave Bayes: T
A Formal View of Boosting
y1) : : : (xm ym)
yi 2 f;1 +1g correct label of instance xi 2 X
for t = 1 : : : T :
construct distribution Dt on f1 : : : mg
given training set
(x1
nd weak hypothesis (rule of thumb)
ht : X ! f;1 +1g
with small error t on Dt:
t =
CS446: Machine Learning
Fall 2013
Problem Set 6 Solution
Hand Out: November 14th , 2013
Handed In: December 5th , 2013
Problem 1: Multi-class Logistic Regression - 10 points
We need to nd
(w)
w .
We see that (w) is dened as:
M
m
log P (Y = yk | xm , w)
(
CS 374
Lab 4 February 4
Spring 2015
This lab covers the subset construction to convert an NFA to a DFA that accepts the same language
and also on how to use the power of NFAs to prove closure under some non-trivial operations.
1. Consider the NFA dened by
CS446: Machine Learning
Spring 2015
Problem Set 3
Handed Out: February 26, 2015
Due: March 12, 2015
• Feel free to talk to other members of the class in doing the homework. We am more concerned that you learn how to
solve the problem than that you demonst