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 words model
1
2
3
4
5
6
7
Words = cfw_john, mary, sex, mone
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 group members that you worked (as well as other sources
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 your own assignments and state
the group members that
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 group members that you worked (as well as other sources
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 group members that you worked (as well as other sources
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 members that you worked (as well as other sources of h
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 state
the group members that you worked (as well as other
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 one
assignment.
Place your names and student numbers on
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 previous assignment. In this dataset, nearby
points tend to r
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 May:
a. Purchased materials on account, $ 470,000.
b. I
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 updating
Posterior
probability
Likelihood
Prior
probabilit
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 line, M2 =
quadratic, M3 = cubic
The posterior over mo
CS340 Machine learning
Lecture 2
Classification and generalization error
Summary of last lecture
Given training data D = cfw_ (x1.y1), , (xN, yN)
Choose right hypothesis class H
linear
quadratic
Depth-2 decision tree
Fit parameters of function given H
CS340 Machine learning
Lecture 4
K-nearest neighbors
Nearest neighbor classifier
Remember all the training data (non-parametric
classifier)
At test time, find closest example in training set,
and return corresponding label
y (x) = yn where n = arg min d
CS340 Machine learning
Information theory
1
Announcements
If you did not get email, contact [email protected]
Newsgroup ubc.courses.cpsc.340
Hw1 due wed bring hardcopy to start of class
Added knnClassify.m, normalize.m
Add/drop deadline tomorrow
2
Informat
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 generalization error)
Suppose X ~ p(), where E[X]=, Var[X]=2
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 cancer, breast cancer or no
cancer), and x are some observab
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 positive
only examples
How far out should
the rectangle go?
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 of 10 except 50 and 20?
Prior
Cannot learn efficient
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 incorporate everything that is known (e.g., D) is
by condit
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|, ) =
=
1
n/2 exp
2
(2 )n/2
n
(xi )2
i=1
1
n/2
exp
n( x
2017-01-04
Management Accounting
BUILDING BLOCKS
CHAPTER 2
Common Business Activities
Service:
Merchandising:
Manufacturing:
1
2017-01-04
Manufacturing Activities
incur Manufacturing or Product costs
Manufacturing Activities
Create three types of invent