Number of Parcels
Size of Parcels
Residential Subdivisions within
City Limits
Parcels Contiguous
"Proposed Division
Condominiums
Community Apartments
Stock Cooperatives
Limited Equity Housing
Cooperatives
Time-Shares
Agricultural Leases
Zoned Industrial o
Short Version of Convex Optimization
Virtually all content taken from Ryan Tibshiranis excellent
10-725 slides
Dan Schwartz
10-701 Recitation: 2017-02-09
1 / 70
Outline
Convexity
Convex Sets
Convex Functions
Optimization
Problem Formalization
Gradients
Gr
10-701 Convexity Notes
Dan Schwartz
2017-02-10
1
Notes from recitation
These are some of the handwritten notes from the recitation on 2017-02-09. Most of the material
from the recitation can be found in the slides available here: http:/www.piazza.com/clas
10-701 Basic
Probability Recitation
Dan Schwartz
2017-01-19
Events, Event Spaces
Events (here denoted ) are possible outcomes of a random
experiment
An event space (here ) is the set of all possible outcomes
Examples:
Coin toss: =
Coin toss: =
Coin tos
CS229 Lecture notes
Andrew Ng
Part V
Support Vector Machines
This set of notes presents the Support Vector Machine (SVM) learning algorithm. SVMs are among the best (and many believe is indeed the best)
o-the-shelf supervised learning algorithm. To tell t
CS229 Lecture notes
Andrew Ng
Supervised learning
Lets start by talking about a few examples of supervised learning problems.
Suppose we have a dataset giving the living areas and prices of 47 houses
from Portland, Oregon:
Living area (feet2 )
2104
1600
2
CS229 Lecture notes
Andrew Ng
Part IX
The EM algorithm
In the previous set of notes, we talked about the EM algorithm as applied to
fitting a mixture of Gaussians. In this set of notes, we give a broader view
of the EM algorithm, and show how it can be ap
CS229 Lecture notes
Andrew Ng
Part IV
Generative Learning algorithms
So far, weve mainly been talking about learning algorithms that model
p(y|x; ), the conditional distribution of y given x. For instance, logistic
regression modeled p(y|x; ) as h (x) = g
CS229 Lecture notes
Andrew Ng
Part X
Factor analysis
When we have data x(i) Rn that comes from a mixture of several Gaussians,
the EM algorithm can be applied to fit a mixture model. In this setting,
we usually imagine problems were the we have sucient da
CS229 Lecture notes
Andrew Ng
Part VI
Regularization and model
selection
Suppose we are trying select among several dierent models for a learning
problem. For instance, we might be using a polynomial regression model
h (x) = g(0 + 1 x + 2 x2 + + k xk ), a
CS229 Lecture notes
Andrew Ng
Part VI
Learning Theory
1
Bias/variance tradeo
When talking about linear regression, we discussed the problem of whether
to fit a simple model such as the linear y = 0 +1 x, or a more complex
model such as the polynomial y =
CS229 Lecture notes
Andrew Ng
Mixtures of Gaussians and the EM algorithm
In this set of notes, we discuss the EM (Expectation-Maximization) for density estimation.
Suppose that we are given a training set cfw_x(1) , . . . , x(m) as usual. Since
we are in
CS229 Lecture notes
Andrew Ng
1
The perceptron and large margin classifiers
In this final set of notes on learning theory, we will introduce a dierent
model of machine learning. Specifically, we have so far been considering
batch learning settings in whic
CS229 Lecture notes
Andrew Ng
The k-means clustering algorithm
In the clustering problem, we are given a training set cfw_x(1) , . . . , x(m) , and
want to group the data into a few cohesive clusters. Here, x(i) Rn
as usual; but no labels y (i) are given.
10-601 Machine Learning, Midterm Exam
Instructors: Tom Mitchell, Ziv Bar-Joseph
Monday 22nd October, 2012
There are 5 questions, for a total of 100 points.
This exam has 16 pages, make sure you have all pages before you begin.
This exam is open book, open
Homework 3
Naive Bayes, Logistic Regression, and Graphical Models
CMU 10-701: Machine Learning (Spring 2016)
OUT: Feb 25, 2016
DUE: Mar 3, 2016, 11:59 PM
Instructions
Collaboration policy: Collaboration on solving the homework is allowed,
after you have t
10-701 Machine Learning
Spring 2016
Final
05/02/2016
Time Limit: 3 hours
Name:
Andrew ID
Instructions:
Fill in your name and Andrew ID above
This exam contains 27 pages (including this cover page) and 6 questions.
Total of points is 201.
You are allowe
Machine Learning (10-701)
Fall 2008
Final Exam
Professor: Eric Xing
Date: December 8, 2008
. There are 9 questions in this exam (18 pages including this cover sheet)
. Questions are not equally dicult.
. This exam is open to book and notes. Computers, PDA
10-701 Final Exam, Spring 2007
1. Personal info:
Name:
Andrew account:
E-mail address:
2. There should be 16 numbered pages in this exam (including this cover sheet).
3. You can use any material you brought: any book, class notes, your print outs of cl
10-701 Introduction to Machine Learning
Homework 2, version 1.0
Due Oct 16, 11:59 am
Rules:
1. Homework submission is done via CMU Autolab system. Please package your writeup and code into
a zip or tar file, e.g., let submit.zip contain writeup.pdf and ps
10-701/15-781, Fall 2006, Final
Dec 15, 5:30pm-8:30pm
There are 9 questions in this exam (15 pages including this cover sheet).
If you need more room to work out your answer to a question, use the back of the page
and clearly mark on the front of the pa
Machine Learning 10-701
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
April 11, 2016
Today:
Learning representations
Readings:
Wall et al., 2003
PCA
ICA
CCA
Latent Dirichlet Allocation
Deep networks
A Tutorial on PCA, J. Schlens