Readings listed in class website
Bayesian point estimation Gaussians Linear Regression Bias-Variance Tradeoff
Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University September 14th, 2009
Carlos Guestrin 2005-2009 1
What about prior
Billion
Recitation 1
Probability Review
Parts of the slides are from previous years recitation and lecture notes
Basic Concepts
l
A sample space S is the set of all possible outcomes of a
conceptual or physical, repeatable experiment. (S can be
finite or infinite
Machine Learning 10-701
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
February 1, 2011
Today:
Generative discriminative
classifiers
Linear regression
Decomposition of error into
bias, variance, unavoidable
Readings:
Mitchell: N
Machine Learning 10-701
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
January 27, 2011
Today:
Readings:
Required:
Mitchell: Nave Bayes and
Logistic Regression
(see class website)
Nave Bayes Big Picture
Logistic regression
Gradien
Machine Learning 10-701
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
January 25, 2011
Today:
Nave Bayes
discrete-valued Xis
Document classification
Gaussian Nave Bayes
real-valued Xis
Brain image classification
Form of dec
Machine Learning 10-701
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
January 20, 2011
Today:
Bayes Classifiers
Nave Bayes
Gaussian Nave Bayes
Readings:
Mitchell:
Nave Bayes and Logistic
Regression
(available on class website)
Machine Learning 10-701
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
January 18, 2011
Today:
Bayes Rule
Estimating parameters
maximum likelihood
max a posteriori
Readings:
Probability review
Bishop Ch. 1 thru 1.2.3
Bishop,
Machine Learning 10-701
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
January 13, 2011
Today:
The Big Picture
Overfitting
Review: probability
Readings:
Decision trees, overfiting
Mitchell, Chapter 3
Probability review
Bishop
Machine Learning 10-701
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
January 11, 2011
Today:
What is machine learning?
Decision tree learning
Course logistics
Readings:
The Discipline of ML
Mitchell, Chapter 3
Bishop, Chapt
Recitation 2
Naive Bayes
Why Bayes Rule?
Definitions:
X: Variables we will observe at test time
Y: Variable we want to predict
What we want to know:
P (Y | X )
What we can compute:
P X Y P Y
P Y X =
PX
There's a catch.
P(X | Y) stands for P X 1 , . , X