CS 486/686 - Assignment #1
Due Date: Wednesday, May 30 by 5pm.
Note: The following exercises are to be done individually. No late assignments will be accepted. Please
hand in your assignment using the
Support Vector
Machines
Example training data
ballet
dancers
height
Training Data
rugby
players
output
ballet
175, 56
rugby
115, 61
5
input
125, 53
6
ballet
.
4
100
125
150 175
weight
200
1
Possible s
CS 486/686 - Assignment #2
Due Date: Wednesday, June 20 by 5pm.
Note: Question 3 may be done in groups of two or three; questions 1 and 2 are to be done
individually. No late assignments will be accep
Decision trees
Start at the root
At each node in the tree, a feature is tested
arcs are labeled with the values of the feature
Leaves contain the classification
Jeeves is a valet to Bertie Wooster
Assignment 2, Question 1
Sample Solution
(a) For each of the Bayesian networks, state whether the network is correct or incorrect, given
the above information. Explain why each network is correct or i
Nave Bayesian network classifier
(a) Suppose Fred decides to construct a nave Bayes classifier from the data. What classifier
will he come up with? Draw the resulting network and show the prior and co
CS 486/686: Introduction to Artificial Intelligence
Assignment 2
Due Date: February 10, 2017 at 11:59pm
Submission Instructions: Assignments are to be submitted through LEARN, in the Dropbox labelled
Assignment 4, Question 1
Sample Solution
(a) Construct a decision network for deciding whether or not to call the police. Show your utility
function and determine the optimal decision for each combina
CS 486/686 - Assignment #4
Due Date: Wednesday, July 25 by 5pm.
Note: No late assignments will be accepted. Please hand in your assignment using the drop boxes
by 5pm on the day that it is due.
#1. (3
Course Overview
The design of automated systems capable of accomplishing complicated tasks is at the heart of computer
science. Abstractly, automated systems can be viewed as taking inputs and produci
Reasoning Over Time
[RN2] Sec 15.1-15.3, 15.5
[RN3] Sec 15.1-15.3, 15.5
CS 486/686
University of Waterloo
Lecture 11: June 5, 2017
Outline
Reasoning under uncertainty over time
Hidden Markov Models
Statistical Learning
[RN2 Sec 20.1-20.2]
[RN3 Sec 20.1-20.2]
CS 486/686
University of Waterloo
Lecture 16: June 21, 2017
Outline
Statistical learning
Bayesian learning
Maximum a posteriori
Maximum
Machine Learning
[RN2] Sec 18.1-18.4
[RN3] Sec 18.1-18.4
CS 486/686
University of Waterloo
Lecture 13: June 12, 2017
Outline
Inductive learning
Decision trees
CS486/686 Lecture Slides (c) 2017 P. Po
Informed Search
[RN2] Sec. 4.1, 4.2
[RN3] Sec. 3.5, 3.6
CS 486/686
University of Waterloo
Lecture 3: May 8, 2017
1
CS486/686 Lecture Slides 2017 (c) P. Poupart
Outline
Using knowledge
Heuristics
Be
Constraint Satisfaction
[RN2] Sec 5.1-5.2
[RN3] Sec 6.1-6.3
CS 486/686
Lecture 4: May 10, 2017
University of Waterloo
1
CS486/686 Lecture Slides (c) 2017 P. Poupart
Outline
What are CSPs?
Standard s
Reasoning Over Time
[RN2] Sec 15.1-15.3, 15.5
[RN3] Sec 15.1-15.3, 15.5
CS 486/686
University of Waterloo
Lecture 12: May 11, 2015
Outline
Reasoning under uncertainty over time
Hidden Markov Models
Uncertainty
[RN2 Sec. 13.1-13.6]
[RN3 Sec. 13.1-13.5]
CS 486/686
University of Waterloo
Lecture 6: May 21, 2015
CS486/686 Lecture Slides (c) 2015 P. Poupart
1
A Decision Making Scenario
You are consi