CS485: Assignment 2 Solutions
1. We will prove that the set A = cfw_0, e1 , e2 , ., en can be shattered by HS n , where ei denotes a unit vector in
Rn . In order to do this, we will show that for an
CS485: Assignment 3 Solutions
1.
(a) True. If S is an -net for H w.r.t. P , then for all h H , if P (h) > , then S h = . Since S S , for
every h H , if P (h) > , S h S h = . Therefore S is an -net for
CS 489/698: Machine Learning,
Winter 2013, Assignment 3
Shai Ben-David
Due date is Thursday March 28, at 4:00pm in class.
Please write clearly (preferably type your assignment), staple the pages toget
CS 489/698: Machine Learning,
Winter 2013, Assignment 2
Shai Ben-David
Due date is Thursday Feb. 14, at 4:00pm in class.
Please write clearly (preferably type your assignment), staple the pages togeth
CS485/685 - Winter 2015
Assignment 3 Solutions
1. (a) Let S := cfw_d(h) : h H. Also, let N = supcfw_|d(h)| : h H. Consider the strings as nodes in
a binary tree. In the general case, we have codes for
Chapter 2
The Runtime of Learning
So far in the book we studied the statistical perspective of learning, namely, how
many samples are needed for learning. In other words, we focused on the amount
of i
CS485 - Winter 2013, Assignment 1
Due Jan. 31st 4pm (in class)
Shai Ben-David, University of Waterloo
The rst part of this assignment deals with very basic notions; What is a
learning algorithm? How d
8
The Runtime of Learning
So far in the book we studied the statistical perspective of learning, namely, how
many samples are needed for learning. In other words, we focused on the amount
of informati
CS485/685 - Fall 2015, Assignment 2
Due Monday, Nov 2, at 11:59 am
Shai Ben-David, University of Waterloo
This assignment is about shattering, VC-dimension, Sauers lemma, -nets
and -approximations. Th
CS485/685 - Fall 2015, Assignment 2
Due Monday, Nov 2, at 11:59 am
Shai Ben-David, University of Waterloo
This assignment is about shattering, VC-dimension, Sauers lemma, -nets
and -approximations. Th
CS485/685 - Winter 2015
Assignment 4 Solutions
1. Consider the procedure F described in the hint. Given S of size m, it divides S into k chunks
(Si , i [k]) of size m1 each and once chunk Sk+1 of size
CS485/685 - Winter 2015
Assignment 2 Solutions
1. (a) Claim: VC-dim(Hkones ) = k. Let A be an arbitrary shattered set. |A| k because the
hypothesis class does not have a member that outputs 1 on more
CS 485/685: Machine Learning, Winter 2015
Assignment 2
Shai Ben-David
Due date is Friday, March 6, at 1pm.
Please write clearly (preferably type your assignment), staple the pages together, and have y
CS 485/685: Machine Learning, Winter 2015
Assignment 3
Shai Ben-David
Due date is Friday March 20, at 1:00pm drop your assignment
in the assignment boxes in MC on the 4th floor or in class.
Please wri
CS485/685 - Winter 2015
Assignment 1 Solutions
1. (a) P = Px C where Px is the marginal distribution and C is the conditional probability over
labels. The loss of any deterministic function g is
X
LP
CS 485/685: Machine Learning, Winter 2015
Assignment 4
Shai Ben-David
Due Friday April 10, at 1:00pm.
Drop your assignment in the assignment boxes in MC on the 4th.
Please write clearly (preferably ty
CS485/685 - Winter 2015, Assignment 1
Due Friday, Feb 13th at 11:59 am
Shai Ben-David, University of Waterloo
This assignment deals with very basic notions; What is a learning algorithm?
How do we eva
Chapter 1
Minimum Description Length
The notions of PAC learnability discussed so far in the book allow the sample sizes
to depend on the accuracy and condence parameters but they are uniform with
res
CS 489
June 30, 2009
Online Learning
The student gets examples one at a time and issues a label prediction, then sees what the correct label
is. Repeats. The measure of success is the # of misspredict
Here are 14 emails. I have classified them into SPAM, NOTSPAM. If I know the sender,
the email has attribute KNOWN SENDER. If I don't know the sender, it has attribute
UKNOWN SENDER.
- EMAIL #1 - SPAM
Machine Learning
CS489/698
Lecture 1: Jan 4th, 2017
Pascal Poupart
Associate Professor
David R. Cheriton School of Computer Science
University of Waterloo
CS489/698 (c) 2017 P. Poupart
1
Machine Learn
Machine Learning:
Foundations and Algorithms
Shai Ben-David and Shai Shalev-Shwartz
DRAFT
2
c Shai Ben-David and Shai Shalev-Shwartz.
i
Preface
The term machine learning refers to the automated detect
Chapter 6
The VC-dimension
In the previous chapter, we decomposed the error of the ERMH rule into approximation error and estimation error. The approximation error depends on the t of
our prior knowle
9
Linear Predictors
In this chapter we will study the family of linear predictors, one of the most
useful families of hypotheses classes. Many learning algorithms that are being
widely used in practic