Use Chapter 3 of K&F as a reference for CSI
Reading for parameter learning: Chapter 12 of K&F
Context-specific independence
Parameter learning: MLE
Graphical Models 10708
Carlos Guestrin
Carnegie Mellon University
October 5th, 2005
Announcements
Homework
Probabilistic
Graphical Models
10-708
Statistical learning with basic
graphical models
Eric Xing
Lecture 9, Oct 10, 2005
Reading: MJ-Chap. 5,6
Learning Graphical Models
The goal:
Given set of independent samples (assignments of
random variables), find the
Advanced informed search
Tuomas Sandholm
!
Computer Science Department"
Carnegie Mellon University
"
Read: Optimal Winner Determination Algorithms.
Sandholm, T. 2006.
Chapter 14 of the book Combinatorial Auctions,
Cramton, Shoham, and Steinberg, e
Advanced informed search
Tuomas Sandholm
!
Computer Science Department"
Carnegie Mellon University
"
Read: Optimal Winner Determination Algorithms.
Sandholm, T. 2006.
Chapter 14 of the book Combinatorial Auctions,
Cramton, Shoham, and Steinberg, e
S. Klepper, Economics 73-100, Fall 2011
Solutions to Exam I
1. If traders are offered a payment of $.40 if they do not make a transaction, they will
need to earn a profit of at least $.40 in order to induce them to make a
transaction. Consider the effect
15-830 Electric Power Systems 2:
Generators, Three-phase Power,
and Power Electronics
J. Zico Kolter
October 9, 2012
1
Generators
Basic AC Generator
Rotating Magnet
Loop of Wire
2
Voltage
Voltage
Voltage
Voltage
Generator operation
3
Rotor - rotating e
15-830 Electric Power Systems 3: Power
Flow and Markets
J. Zico Kolter
October 23, 2012
1
Power Flow in AC Networks
Basic question: how is electricity supplied in the grid?
If these generators produce this much power, how much current
will ow through pa
15-830 Electric Power Systems 1: DC and
AC Circuits
J. Zico Kolter
October 2, 2012
1
Lawrence Livermore
National Laboratory
Estimated U.S. Energy Use in 2010: ~98.0 Quads
Solar
0.11
Net Electricity
Imports
0.01
8.44
Nuclear
8.44
7.52
2.49
19.13
Hydro
2.51
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
September 4, 2012
Today:
What is machine learning?
Decision tree learning
Course logistics
Homework 1 handed out
Readings:
The Discipline of ML
Mitchell,
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
September 6, 2012
Today:
The Big Picture
Overfitting
Review: probability
Readings:
Decision trees, overfiting
Mitchell, Chapter 3
Probability review
Bishop
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
September 11, 2012
Today:
Bayes Rule
Estimating parameters
maximum likelihood
max a posteriori
many of these slides are derived
from William Cohen, Andrew
M
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
September 13, 2012
Today:
Bayes Classifiers
Nave Bayes
Gaussian Nave Bayes
Readings:
Mitchell:
Nave Bayes and Logistic
Regression
(available on class website
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
September 18, 2012
Today:
Nave Bayes
discrete-valued Xis
Document classification
Gaussian Nave Bayes
real-valued Xis
Brain image classification
Form of d