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 Mell
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 independ
Advanced informed search
Tuomas Sandholm
!
Computer Science Department"
Carnegie Mellon University
"
Read: Optimal Winner Determination Algorithms.
Sandholm, T. 2006.
Chapter 14 of the book Comb
Advanced informed search
Tuomas Sandholm
!
Computer Science Department"
Carnegie Mellon University
"
Read: Optimal Winner Determination Algorithms.
Sandholm, T. 2006.
Chapter 14 of the book Comb
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
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
Vol
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 produ
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
Im
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
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, ove
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 o
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 Ba
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