Course Material Machine Learning
Exercise Sheets
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Introducing Matlab
[PDF]
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Maximum Likelihood Estimation
[PDF]
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Linear Discriminant Analysis
[PDF]
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Perceptron
[PDF]
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Boosting
[PDF]
[USPS data]
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Support Vector Machines
[PDF]
[svmfiles.zip]
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Regression
[PDF]
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Kernel Methods
[PDF]
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Entropy
[PDF]
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PAC Learning and VC Dimension
[PDF]
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Clustering
[PDF]
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Mean Field Methods
[PDF]
Slides
Complete Sets of Slides
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Primer: Probability and Statistics
[PDF]
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The EM Algorithm
[PDF]
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Support Vector Machines
[PDF]
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Primer: Information Theory
[PDF]
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Bayesian Estimation
[PDF]
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Clustering
[PDF]
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Jackknife and Bootstrap
[PDF]
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Optimization of Functionals (Variational Calculus)
[PDF]
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Regression
[PDF]
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Precision/Recall and ROC
[PDF]
Other Topics Large & Not So Large
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Counting Dichotomies
[PDF]
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Ensemble Methods
[PDF]
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Histograms and Sample Size
[PDF]
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Hypothesis Testing
[PDF]
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PAC Learning
[PDF]
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Projection Methods
[PDF]
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Supervised vs Unsupervised Learning
[PDF]
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Student's Theses
Theses I supervised at ETH Zurich.
Model Order Selection: Criteria, Inference Strategies, and an Application to Biclustering.
by
Patrick Pletscher.
Master Thesis, ETH Zurich, 2007.
[
PDF]
Clustering Rank Data.
by
Ludwig Busse.
Research Project, ETH Zurich, 2007.
Feature Extraction for Bayesian Order-Adaptive Clustering.
by Samuel Braendle.
Term Thesis, ETH Zurich, 2007.
[
PDF]
Classification of Radar Data Based on Constrained Agglomerative Segmentation.
by Sarah Gugl.
Term Thesis, ETH Zurich, 2006.
Course Pages