COMPSCI 74.436
UNIVERSITY OF MANITOBA
Midterm
Winter 2005
COMPUTER SCIENCE
Machine Learning
Date:
Time:
Room:
Friday, 10 March 2005
15:30 - 16:20
EITC E2-165, University of Manitoba
(Time allowed: 50 Minutes)
NOTE:
Attempt all questions.
This is a closed
Linear Algebra
Vectors
one dimensional array.
If not specified, assume a column
vector.
Matrices
Two dimensional array.
Typically denoted with capital letters.
Transposition
Transposing a matrix swaps columns and rows.
Useful facts about transposition
Dot
Beta prior distribution
Bayesian learning procedure
Step 1: Given a collection of data D= cfw_1 , 2 , , ,
write down the expression for the likelihood
Step 2: specify a prior: ()
Step 3: compute the posterior:
Bayesian learning for thumbtack
compute th
oMCowa is CBWWMH
. . an vohe
Linear Regressnon
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Assumes the following functional form for P(Y | X):
L d1
P(Y QIX) = 1 / 9" lg
exp(w0 +
The lecture notes are a compilation and edition from many sources.
The instructor does not claim intellectual property or ownership of the
lecture notes. Please do not distribute the lecture notes.
Training
Training
Images
Training
Training
Images
Testing
Simple Example: Thumbtack
- P is a Bernoulli distribution
P(X 2W9) = 16
Ll l
Tosses are IID
independent events
._._\_'_
identically distributed according to the same distribution
ha \ Learning Problem
Given toss examples sampled from P
D = cfw_331,5c2
Cross-validation
Data are often limited
Cross-validation creates S groups of data, use S 1
to train, other to validate
Extreme case leave-one-out cross-validation (LOO-CV): S
is the number of training data instances Controlling Over-fitting: Regulariza
Derivative w.r.t. a vector
Given a vector x, and a function f(x), how can
Example
Useful facts
Suppose
and
are vectors,
is a square matrix
Two Viewpoints of Probability
coin is 50%
The probability of raining tomorrow is 10%
(Discrete) Random Variable
A ra
COMP 4360: Machine Learning
Winter 2017
Homework 4: Due Apr 10 at 3:00pm, 25 points in total
General guidelines for homeworks:
You are encouraged to meet with other students to discuss the homework, but all write-ups
must be done on your own. Do not take
COMP 4360: Machine Learning
Winter 2017
Homework 1: Due Feb 17 at 3:00pm, 40 points in total
General guidelines for homeworks:
You are encouraged to meet with other students to discuss the homework, but all write-ups
must be done on your own. Do not take
COMPSCI 74.436
UNIVERSITY OF MANITOBA
Winter 2003
COMPUTER SCIENCE
Machine Learning
Paper No.: 800
Examiners: Jacky Baltes
Date:
24th April 2003
Time:
18:00
Room:
Frank Keneddy Center, Gold Gym
(Time allowed: 180 Minutes)
NOTE:
Attempt all questions.
This
COMPSCI COMP 4360
UNIVERSITY OF MANITOBA
Midterm
Winter 2011
COMPUTER SCIENCE
Machine Learning
Date:
Time:
Room:
Friday, 2nd March 2011
15:30 - 16:20
EITC E2-165, University of Manitoba
(Time allowed: 50 Minutes)
NOTE:
Attempt all questions.
This is a clo
COMP COMP4360
UNIVERSITY OF MANITOBA
Final Examination
Winter 2011
COMPUTER SCIENCE
Machine Learning
Paper No.:
Examiners:
Date:
Time:
Room:
Jacky Baltes
Monday, 11th April 2011
18:00
Frank Kennedy, Brown Gym (184-208)
(Time allowed: 180 Minutes)
NOTE:
At
COMPSCI COMP 4360
University of Manitoba
Midterm
Winter 2012
COMPUTER SCIENCE
Machine Learning
Date:
Time:
Room:
Friday, 9th March 2012
15:30 - 16:20
EITC E2-304, University of Manitoba
(Time allowed: 50 Minutes)
NOTE:
Attempt all questions.
This is a clo
COMPSCI COMP 4360
UNIVERSITY OF MANITOBA
Midterm
Winter 2007
COMPUTER SCIENCE
Machine Learning
Date:
Time:
Room:
Friday, 2nd March 2007
15:30 - 16:20
EITC E2-165, University of Manitoba
(Time allowed: 50 Minutes)
NOTE:
Attempt all questions.
This is a clo
COMPSCI 74.436
UNIVERSITY OF MANITOBA
Final Examination
Winter 2005
COMPUTER SCIENCE
Machine Learning
Paper No.: 444
Examiners: Jacky Baltes
Date:
18 April 2006
Time:
9:00
Room:
University College Great Hall (25 - 48)
(Time allowed: 180 Minutes)
NOTE:
Att
COMPSCI 74.436
UNIVERSITY OF MANITOBA
Final Examination
Winter 2004
COMPUTER SCIENCE
Machine Learning
Paper No.:
Examiners:
Date:
Time:
Room:
234
Jacky Baltes
14 April 2004
18:00
University College, Great Hall
(Time allowed: 180 Minutes)
NOTE:
Attempt all
COMPSCI 74.436
UNIVERSITY OF MANITOBA
Midterm
Winter 2003
COMPUTER SCIENCE
Machine Learning
Date:
Time:
Room:
8 March 2004
15:30 - 16:30
Armes Building 115, University of Manitoba
(Time allowed: 50 Minutes)
NOTE:
Attempt all questions.
This is a closed bo
COMPSCI 74.436
UNIVERSITY OF MANITOBA
Winter 2003
COMPUTER SCIENCE
Machine Learning
(Time allowed: 50 Minutes)
NOTE:
Attempt all questions.
This is a closed book examination.
Use of calculators is permitted.
Show your work to receive full marks.
SURNAME:
COMPSCI 74.436
UNIVERSITY OF MANITOBA
Midterm
Winter 2004
COMPUTER SCIENCE
Machine Learning
Date:
Time:
Room:
9 March 2005
15:30 - 16:30
Armes Building 115, University of Manitoba
(Time allowed: 50 Minutes)
NOTE:
Attempt all questions.
This is a closed bo
Chain Rule
The joint probability can be specified with conditional
probability
PgXEY) = PgX|g)P(Y) = P(Y|X)I_Q()
If we have more RVs:
You XL XV), lbw X1 X) My x)
1?li XL v Xvi)-P(7\2/X5~ xv.) my x) Conditional Probability Example
Assume we have a dark