10-701 Midterm Exam, Fall 2007
1. Personal info:
Name:
Andrew account:
E-mail address:
2. There should be 17 numbered pages in this exam (including this cover sheet).
3. You can use any material yo
Number of Parcels
Size of Parcels
Residential Subdivisions within
City Limits
Parcels Contiguous
"Proposed Division
Condominiums
Community Apartments
Stock Cooperatives
Limited Equity Housing
Cooperat
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 Fu
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
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
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 bes
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
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 broa
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 inst
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,
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
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
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
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
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.
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 a
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) a
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
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 yo
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 ta