10-701, Fall 2008, Homework 1 Solution
September 25, 2008
1
Probability [20 Points, Jerry]
1. (8 pts) Andy has an old friend who has two children, but he dont know their genders. (a) What is the probability that they are a girl and a boy? (Hint: For this

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

Machine Learning (10-701)
Fall 2008
Homework #4 Solution
Professor: Eric Xing Due Date: November 10, 2008
1
Expectation Maximization (EM) [24 Points, Mark]
The expectation maximization (EM) algorithm is one of the most important tools in machine learning.

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

Recitation
10-701/15-781, Fall 2008
PCA
By Hanghang Tong ([email protected])
Some Parts of the slides are from previous years recitation and lecture notes
1
Roadmap
PCA: Maximize projected variance
PCA: Minimize re-construction error
PCA vs. SVD
PCA vs. Feature

Recitation
10-701/15-781, Fall 2008
Final Review
By Hanghang Tong ([email protected])
Some Parts of the slides are from previous years recitation and lecture notes
1
Chaptering Classifiers (binary case)
How many classifiers have we learned in class?
Gaussian Bay

Note to other teachers and users of these slides. Andrew would be delighted if you found this source material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. PowerPoint originals are ava

A Formal Description of Boosting given training set (x1, y1), . . . , (xm, ym) yi cfw_1, +1 correct label of instance xi X for t = 1, . . . , T : construct distribution Dt on cfw_1, . . . , m nd weak classier (rule of thumb) ht : X cfw_1, +1 with small er

Recitation
10-701/15-781, Fall 2008
Logistic Regression
By Hanghang Tong ([email protected])
Some Parts of the slides are from previous years recitation and lecture notes
1
Why (binary) LR is a linear classifier
Gradient Ascent
How & What is the intuition Over-f

Decision Trees and Information Gain
Slides are modified based on Prof. Andrew Moores tutorial at http:/www.cs.cmu.edu/~awm/tutorials
Copyright Andrew W. Moore Slide 1
Entropy and Information Gain Learning an unpruned decision tree recursively Training Set

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

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

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

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

Recitation 1
Probability Recap
What we will cover
Basic probability
Definitions and Axioms. Random Variables PDF and CDF.
Joint distributions. Some common distributions. Independence. Conditional distributions.
Probability
Real world - Full of uncer

Solution to 10-701/15-781 Midterm Exam Fall 2004
1
1
Introductory Probability and Statistics (12 points)
(a) (2 points) If A and B are disjoint events, and P r(B ) > 0, what is the value of P r(A|B )? Answer: 0 ( Note that: A B = )
(b) (2 points) Suppose

Machine Learning
10-701/15-781, Fall 2008 10- 701/15-
Computational Learning Theory II
Eric Xing
Lecture 11, October 13, 2008
Reading: Chap. 7 T.M book, and outline material
Eric Xing @ CMU, 2006-2008 1
Last time: PAC and Agnostic Learning
Finite H, assu

Machine Learning
10-701/15-781, Fall 2008 10- 701/15-
Computational Learning Theory
Eric Xing
Lecture 10, October 8, 2008
Reading: Chap. 7 T.M book
Eric Xing @ CMU, 2006-2008 1
Generalizability of Learning
In machine learning it's really generalization e

Machine Learning
10-701/15-781, Fall 2008 10- 701/15-
Ensemble methods Boosting from Weak Learners
Eric Xing
Lecture 9, October 6, 2008
Reading: Chap. 14.3 C.B book
Eric Xing @ CMU, 2006-2008 1
Project proposal due this Wed Mid term
Eric Xing @ CMU, 200