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 drop boxes by 5pm on the day that it is due.
#1. (30 p
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 2 Submissions in the Assignment 2 folder. La
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. On some days, Bertie likes to
play tennis and asks Je
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 accepted. Please hand in your assignment using the drop
boxe
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 incorrect.
(i) Network (i) is a correct representation.
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 conditional
probability tables associated with each node
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. (35 points) Consider once again the Bayesian network for
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 combination of values for the evidence
(observable) variables.
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 producing outputs towards the
realization of some objectives.
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
Dynamic Bayesian Networks
2
CS486/686 Lecture Slides (
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 likelihood
Learning from complete Data
CS486/686 Lect
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. Poupart
2
What is Machine Learning?
Definition:
A compu
Probabilistic Reasoning
[RN2] Sections 14.1, 14.2
[RN3] Sections 14.1, 14.2
University of Waterloo
CS 486/686
Lecture 7: May 23, 2017
Outline
Review probabilistic inference,
independence and conditional
independence
Bayesian networks
What are they
Wha
Utility Theory
[RN2] Sect 16.1-16.3
[RN3] Sect 16.1-16.3
CS 486/686
University of Waterloo
Lecture 9: May 29, 2017
1
CS486/686 Lecture Slides (c) 2017 P.Poupart
Outline
Decision making
Utility Theory
Decision Trees
Chapter 16 in R&N
Note: Some of the
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
Best-first search
Greedy best-first search
A* search
O
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 search and CSPs
Improvements
Backtracking
Backtrackin
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
Dynamic Bayesian Networks
2
CS486/686 Lecture Slides (
Decision Networks
[RN2] Sections 16.5, 16.6
[RN3] Sections 16.5, 16.6
CS 486/686
University of Waterloo
Lecture 10: June 4, 2015
Outline
Decision Networks
Aka Influence diagrams
Value of information
2
CS486/686 Lecture Slides (c) 2015 P. Poupart
1
Deci
Utility Theory
[RN2] Sect 16.1-16.3
[RN3] Sect 16.1-16.3
CS 486/686
University of Waterloo
Lecture 9: June 2, 2015
1
CS486/686 Lecture Slides (c) 2015 P.Poupart
Outline
Decision making
Utility Theory
Decision Trees
Chapter 16 in R&N
Note: Some of the
Probabilistic Reasoning
[RN2] Sections 14.1, 14.2
[RN3] Sections 14.1, 14.2
University of Waterloo
CS 486/686
Lecture 7: May 26, 2015
Outline
Review probabilistic inference,
independence and conditional
independence
Bayesian networks
What are they
Wha
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 considering to buy a used car
Is it in good condition?
How m