Project Guidelines
Projects!
Goal: apply machine learning to an interesting task
Proposal (due tomorrow!): 1pg
Who is in your group
Your task (and why is it interesting?)
Where did/will you get your d
Clustering Part 2
EECS 349 Spring 2015
Expectation Maximization
Learning parameters in Bayes Nets is easy if data is
complete
Just counting
But what about missing data?
We could use our standard missi
Online Generation of Locality
Sensitive Hash Signatures
BENJAMIN VAN DURME & ASHWIN LALL
Data Overload
Our access to data is growing fast
Benjamin Van Durme & Ashwin Lall
ACL 2010
Data Overload
Our
Machine Learning
Genetic Algorithms
Doug Downey, adapted from Bryan Pardo, Machine Learning EECS 349 Fall 2007
Genetic Algorithms
Developed: USA in the 1970s
Early names: J. Holland, K. DeJong, D. G
Machine Learning
Greedy Local Search
With slides from Bryan Pardo, Stuart Russell
ML in a Nutshell
Every machine learning algorithm has three
components:
Representation
E.g., Decision trees, instan
Basics of Probability
Northwestern EECS 349
Doug Downey
Events
Event space
E.g. for dice, = cfw_1, 2, 3, 4, 5, 6
Set of measurable events S 2
E.g.,
= event we roll an even number = cfw_2, 4, 6 S
S m
Machine Learning
Clustering
Some slides from B. Pardo, P. Domingos
First, some epistemology
There are known knowns. These are things we know
that we know.
Databases!
There are known unknowns. That
Bayes Net Learning and Logistic
Regression
EECS 349 Spring 2015
Learning in Bayes Nets the upshot
Where does the structure come from?
Write it down (BNs most useful in this case), or
Learn it automati
Inductive Learning and Decision Trees
Doug Downey
EECS 349 Winter 2014
with slides from Pedro Domingos, Bryan Pardo
Outline
Announcements
2
Homework #1 assigned
Have you completed it?
Inductive learni
Basics of Statistical Estimation
Doug Downey, Northwestern EECS 395/495, Fall 2014
(several illustrations from P. Domingos, University of Washington CSE)
Bayes Rule
P(A | B) = P(B | A) P(A) / P(B)
Exa
Machine Learning
Instance-based learning
(with slides/ideas from Bryan Pardo, Pedro Domingos, and Andrew Moore)
1
Nearest Neighbor Classifier
Example of instance-based (a.k.a case-based)
learning
Th