CSE515: Statistical Methods in Computer Science
Winter, 2016
Homework 2
Due at noon on February 10, 2016
G UIDELINES : You can brainstorm with others, but please solve the problems and write up
the an
CSE515: Statistical Methods in Computer Science
Winter, 2016
Homework 1
Due at noon on January 27, 2016
G UIDELINES : You can brainstorm with others, but please solve the problems and write up
the ans
CSE446: Decision Trees
Winter 2016
Ali Farhadi
Slides adapted from Carlos Guestrin, Andrew Moore, and Luke ZeGelmoyer
AdministraIve stu
Oce hours
Discussion b
Assignment 1
CSE 446: Machine Learning
University of Washington
Due: October 12, 2017
You will submit the following files or documents for this homework, compressed into a gzipped
tarball named A1.tgz
Assignment 3
CSE 446: Machine Learning
University of Washington
Due: November 5, 2017
November 3, 2017: updates in red.
You will submit the following files or documents for this homework, compressed i
Assignment 2
CSE 446: Machine Learning
University of Washington
Due: October 24, 2017
You will submit the following files or documents for this homework, compressed into a gzipped
tarball named A2.tgz
Project Part 2: Dataset Review
CSE 446: Machine Learning
University of Washington
Deadline: October 26, 2017 October 30, 2017
October 23, 2017: updates in red.
For each part of the project, your team
CSE446: Linear Regression
Spring 2017
Ali Farhadi
Slides adapted from Carlos Guestrin and Luke Zettlemoyer
Prediction of continuous variables
Billionaire says: Wait, thats not what I meant!
You say:
CSE446: Clustering and EM
Spring 2017
Ali Farhadi
Slides adapted from Carlos Guestrin, Dan Klein, and Luke Zettlemoyer
Clustering
Clustering systems:
Unsupervised learning
Detect patterns in unlabe
CSE446: Decision Trees
Spring 2017
Ali Farhadi
Slides adapted from Carlos Guestrin, Andrew Moore, and Luke Zettelmoyer
Administrative stuff
Office hours
Discussion board
Anonymous feedback form
Conta
CSE446: Linear Regression
Spring 2017
Ali Farhadi
Slides adapted from Carlos Guestrin and Luke Zettlemoyer
Prediction of continuous variables
Billionaire says: Wait, thats not what I meant!
You say:
CSE446: Decision Tree
Part2
Winter 2016
Ali Farhadi
Slides adapted from Carlos Guestrin, Andrew Moore, and Luke ZeHelmoyer
So far
Decision trees
They will
CSE446: Perceptron
Winter 2016
Ali Farhadi
Slides adapted from Dan Klein, Luke ZeElemoyer
Who needs probabiliHes?
Previously: model data
with distribuHons
Joint: P
CSE515: Statistical Methods in Computer Science
Winter, 2016
Homework 3
Due at noon on February 24, 2016
G UIDELINES : You can brainstorm with others, but please solve the problems and write up
the an
CSE515: Statistical Methods in Computer Science
Winter, 2016
Homework 4
Due at noon on March 9, 2016
G UIDELINES : You can brainstorm with others, but please solve the problems and write up
the answer
CSE 446
Machine Learning
Instructor: Ali Farhadi
[email protected]
Slides adapted from Pedro Domingos, Carlos Guestrin, and Luke Zettelmoyer
Logistics
Instructor: Ali Farhadi
Email: [email protected]
Offi
CSE446: Logis-c Regression
Winter 2016
Ali Farhadi
Slides adapted from Carlos Guestrin and Luke ZeElemoyer
Lets take a(nother) probabilis-c approach!
Previously: direc
CSE446: SVMs
Winter 2016
Ali Farhadi
Slides adapted from Carlos Guestrin, and Luke ZeDelmoyer
Linear classiers Which line is beDer?
< 0
0
w!x +
w
<0
0
0
w!x +
w
w!x
CSE446: Nave Bayes
Winter 2016
Ali Farhadi
Slides adapted from Carlos Guestrin, Dan Klein, Luke ZeIlemoyer
Supervised Learning: nd f
Given: Training set cfw_(xi, yi) |
CSE446: Point Es.ma.on
Winter 2016
Ali Farhadi
Slides adapted from Carlos Guestrin, Dan Klein, and Luke ZeFlemoyer
Your rst consul.ng job
A billionaire from the su
CSE446: Kernels
Winter 2016
Ali Farhadi
Slides adapted from Carlos Guestrin, and Luke ZeClemoyer
Top 3:
#3 Akash Gupta
#2 Karanbir Singh
#1 Pascale Wallace Patterson
What if
CSE446: Nave Bayes
Spring 2017
Ali Farhadi
Slides adapted from Carlos Guestrin, Dan Klein, Luke Zettlemoyer
Supervised Learning: find f
Given: Training set cfw_(xi, yi) | i = 1 n
Find: A good approx
CSE446: SVMs
Spring 2017
Ali Farhadi
Slides adapted from Carlos Guestrin, and Luke Zettelmoyer
Linear classifiers Which line is better?
Pick the one with the largest margin!
Margin for point j:
wx = i
CSE 446 Machine Learning, Spring 2017
Homework 4
Due: Thursday, June 1, 11:59 PM
Please submit a PDF of your answers and your programming implementation to the class dropbox:
https:/catalyst.uw.edu/co
CSE446 Machine Learning, Spring 2017: Homework 2
Due: Thursday, May 4th , beginning of class
Start Early! Also, typed solutions (specifically those in LaTeX) are preferred to hand-written solutions.
A
CSE446 Machine Learning, Spring 2017: Homework 1
Due: Thursday, April 20th , beginning of class
Start Early! Also, typed solutions (specifically those in LaTeX) are preferred to hand-written solutions
Whats the Perceptron
Optimizing?
Machine Learning CSE446
Carlos Guestrin
University of Washington
May 1, 2013
Carlos Guestrin 2005-2013
The Perceptron Algorithm
n
n
[Rosenblatt 58, 62]
Classification
Regularization
Machine Learning CSE446
Carlos Guestrin
University of Washington
2005-2013 Carlos Guestrin
April 10, 2013
1
Regularization in Linear Regression
n
Overfitting usually leads to very large