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Gaussians
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Modeling continuous variables
The Gaussian Model
MLE for Gaussians
The Bayesian way
Modeling continuous variables
The biased coin example involved discrete (Be
Linear Methods for Classication
Linear Methods for Classication
Jia Li
Department of Statistics The Pennsylvania State University
Email: [email protected] http:/www.stat.psu.edu/jiali
Jia Li
http:/ww
An introduction to regression
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if you found this source material useful in
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1.13. Linear and quadratic discriminant analysis
Linear discriminant analysis (lda.LDA) and quadratic discriminant analysis (qda.QDA) are two classic
classifie
Lectures /
Bias Variance
See also the unusually clear wikipedia article.
This nice applet for polynomial regression is a lot of fun (as far as regression applets go). It illustrates the fundamental tr
Lectures /
Classification
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Classification
Generative vs. Discriminative Approaches
Generative approach
Discriminative approach
Classification
The goal of classification is to learn
10/28/2014
1.7. Naive Bayes scikit-learn 0.15.2 documentation
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1.7. Naive Bayes
Naive Bayes methods are a set of supervised learning algorithms based on applyin
Logistic Regression
What is the logistic curve? What is the base of the natural logarithm? Why do statisticians prefer
logistic regression to ordinary linear regression when the DV is binary? How are
Logistic Regression
Logistic Regression
Jia Li
Department of Statistics The Pennsylvania State University
Email: [email protected] http:/www.stat.psu.edu/jiali
Jia Li
http:/www.stat.psu.edu/jiali
Log
Lectures /
Regression
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Continuous variable prediction
Linear regression
Multivariate case
What happened to the constant term?
What about polynomial or non-linear regression?
MLE
MAP
Lectures /
Naive Bayes
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Naive Bayes Model
Estimating
Estimating
MLE vs. MAP
Examples
Naive Bayes Model
The Naive Bayes classifier is an example of the generative approach: we will m
Lectures /
Logistic
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Logistic Regression
1-D Example
2-D Example
Computing MLE
Computing MAP
Multinomial Logistic Regression
Naive Bayes vs. Logistic Regression
Linear boundary for
Lectures /
Local Learning
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The Supervised Learning Problem
Local Learning
Nearest Neighbor Methods
Non-parametric models
1-NN Consistency, Bias vs Variance
K-Nearest Neighbors
Kerne
Lectures /
EM
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Mixtures of Gaussians and Expectation Maximization
Gaussian Mixtures
Expectation maximization
EM in general
Mixtures of Gaussians and Expectation Maximization
The K-m
Lectures /
Point Estimation
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Point Estimation Basics: Biased Coin
Maximum Likelihood Estimation (MLE) for the coin
Learning guarantees
The Bayesian way
Point Estimation Basics: Bias
Recitations /
Bias Variance
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1. Bias-Variance Decomposition in Regression
1. Bias-Variance Decomposition in Regression
Suppose we want to fit the following regression prediction mod
6.034 Recitation October 23: Nearest Neighbors, Drawing decision boundaries
Bob Berwick
Boundary lines are formed by the intersection of perpendicular bisectors of every pair of points.
Using pairs of
Lectures /
Probability Review
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1. Introductory Example
2. Combining Events
3. Random Variables
4. Probability Distributions
5. Joint Distributions
6. Marginalization
7. PDFs and CDF
Lectures /
SV Ms
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Support Vector Machines
Hyperplanes and Margins
The Dual View
Kernels
Non-separable case
Solving SVMs
Support Vector Machines
Historically, Support Vector Machines