Steinborn Homes manufactures prefabricated chalets in Colorado. The company uses a perpetual
inventory system and a job cost system in which each chalet is a job. The following events
occurred during
CPSC 340 Assignment 2 (due October 7)
K-Nearest Neighbours, Random Forests, K-Means, Density-Based Clustering
1
K-Nearest Neighbours
In this question we revisit the citiesSmall dataset from the previo
CPSC 340 Assignment 1 (due September 23)
Data Exploration, Decision Trees, Training and Testing, Naive Bayes
You can work on your own or in a group of 2. If you work in a group, please only hand in o
CPSC 340 Assignment 5 (due November 27)
Sparse Latent Factors, Recommender Systems, MDS, Neural Networks
You can work in groups on the assignments. However, please hand in your own assignments and st
CPSC 340 Assignment 3 (due October 23rd)
Clustering, Item Recommendation, Linear Regression
You can work in groups on the assignments. However, please hand in your own assignments and state
the group
CPSC 340 Assignment 4 (due November 13)
Regularized Logistic Regression, PCA, Outlier Detection
You can work in groups on the assignments. However, please hand in your own assignments and state
the g
CPSC 340 Assignment 2 (due October 2nd)
Frequency-Based Supervised Learning, K-Means Clustering
You can work in groups on the assignments. However, please hand in your own assignments and state
the g
CPSC 340 Assignment 1 (due September 18)
Summary Statistics and Data Visualization, Decision Tress and Cross-Validation, Probability
You can work in groups on the assignments. However, please hand in
CPSC 340 Assignment 6 (due December 4)
Multi-Class Logistic, Label Propagation with Random Walks
You can work in groups on the assignments. However, please hand in your own assignments and state
the
Ties That Constrict English as a Trojan Horse Essay
According to the David Cookes (1987) article Ties That Constrict
English as a Trojan Horse, he argued the spread of English was a positive
developme
import static java.lang.System.out;
/
/
/
/
this may come in handy again:
System.out.println("decDigit is " + decDigit);
System.out.println("16^(exp-) = " + Math.pow(16, exp);
System.out.println("Dec
CSC240 Winter 2014 Homework Assignment 4
Due Monday, March 24
For this assignment, your proofs do not have to be formal (i.e. you dont have to write
separate lines with numbers and justifications on e
CSC240 Winter 2014 Homework Assignment 3
Due Monday, March 10
For this assignment, your proofs do not have to be formal (i.e. you dont have to write
separate lines with numbers and justifications on e
CSC240 Winter 2014 Homework Assignment 5
Due Friday, April 4
1. Let L be the language over alphabet cfw_a, b associated with the regular expression
r = ab aa + bba ab.
Find a nondeterministic finite a
CSC240 Winter 2014 Homework Assignment 1
due Wednesday January 22, 2014, 11:15am
1. Recall the rules of the game of chess. If you are not familiar with chess, then refer to
http:/en.wikipedia.org/wiki
CSC240 Winter 2014 Homework Assignment 2
For this assignment, your proofs do not have to be formal (i.e. you dont have to write
separate lines with numbers and justifications on each line). However th
FMGT 3720
Advanced Computer Applications
Introduction & Background
Instructor
Lata Kochhar, CPM, PID, M.Phil., M.Com.
[email protected]
Course Format
Lecture
Lab
I will Demo; you can start assi
CPSC213/2013W2 Midterm EXTRA Practice
DEC/HEX/BIN NUMERACY
1. Convert into decimal:
1a.
1b.
1c.
1d.
0x33
0x57
0xaf
0x7a
1e. 0x1234
1f. 0x69bd
1g. 0x1a64
1h. 0xdead
2. Convert into hex numbers of the s
CS340 Machine learning
Nave Bayes classifiers
Document classification
Let Y cfw_1,C be the class label and x cfw_0,1d
eg Y cfw_spam, urgent, normal,
xi = I(word i is present in message)
Bag of word
CS340 Machine learning
Midterm review
Topics
Bayesian statistics
Information theory
Decision theory
kNN not on exam
Sampling distributions (confidence intervals etc) not on exam
Bayesian belief upd
CS340 Machine learning
Bayesian model selection
Bayesian model selection
Suppose we have several models, each with
potentially different numbers of parameters.
Example: M0 = constant, M1 = straight
CS340 Machine learning
Bayesian statistics 3
1
Outline
Conjugate analysis of and 2
Bayesian model selection
Summarizing the posterior
2
Unknown mean and precision
The likelihood function is
p(D|,
CS340 Machine learning
Bayesian statistics 2
1
Binomial distribution (count data)
X ~ Binom(, N), X cfw_0,1,N
N
x
P (X = x|, N ) =
theta=0.500
x (1 )N x
theta=0.250
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
CS340 Machine learning
Bayesian statistics 1
Fundamental principle of Bayesian statistics
In Bayesian stats, everything that is uncertain (e.g.,
) is modeled with a probability distribution.
We inco
CS340
Bayesian concept learning cont'd
Kevin Murphy
Prior p(h)
X=cfw_60,80,10,30
Why prefer multiples of 10 over even
numbers?
Size principle (likelihood)
Why prefer multiples of 10 over
multiples
CS340:
Bayesian concept learning
Kevin Murphy
Based on Josh Tenenbaums PhD
thesis (MIT BCS 1999)
Concept learning (binary
classification) from positive and
negative examples
Concept learning from posi
CS340 Machine learning
Decision theory
1
From beliefs to actions
We have briefly discussed ways to compute p(y|x),
where y represents the unknown state of nature (eg.
does the patient have lung cance
CS340 Machine learning
Lecture 5
Notes
Outline
HW1
Finish KNN
Start info theory
Office hours Tue 4-5, CS187
Standard error
Suppose we want to estimate E[X] from n samples,
X1, , Xn (eg X is genera