Difference between revisions of "MLC"
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* + Machine learning introduction, paradigm, estimation ... [Matt] | * + Machine learning introduction, paradigm, estimation ... [Matt] | ||
| − | ==== Supervised ==== | + | ==== Supervised Learning ==== |
---- | ---- | ||
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====== Kernel methods ====== | ====== Kernel methods ====== | ||
| − | ~ SVM and Kernel methods [Matt (?) + Luigi (?) + (?)] | + | * ~ SVM and Kernel methods [Matt (?) + Luigi (?) + (?)] |
| − | ==== Unsupervised ==== | + | ==== Unsupervised Learning==== |
---- | ---- | ||
======Distributions of data====== | ======Distributions of data====== | ||
* + Feature selection and projection (PCA, ICA, ...) [Rossella (?)+ Simone] | * + Feature selection and projection (PCA, ICA, ...) [Rossella (?)+ Simone] | ||
| − | * | + | |
| − | + | ======Clustering====== | |
| + | * + Clustering galore [Davide (?)] | ||
====== Graphical models ====== | ====== Graphical models ====== | ||
* ~ Bayesian networks [Matt + Luigi (?)] | * ~ Bayesian networks [Matt + Luigi (?)] | ||
| − | ======Time | + | ======Non Parametric techniques====== |
| + | * Kernel based density estimation, ... [Rossella (?) + Luigi (?) + Simone] | ||
| + | |||
| + | ======Time Varying Models====== | ||
* + Markov Chains [Matt + Bernardo (?) +Simone] | * + Markov Chains [Matt + Bernardo (?) +Simone] | ||
* + Hidden Markov Models [Matt + Simone (?)] | * + Hidden Markov Models [Matt + Simone (?)] | ||
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==== Learning Techniques and Methodologies ==== | ==== Learning Techniques and Methodologies ==== | ||
---- | ---- | ||
| − | + | * ~ Cross-Validation, Bootstrapping, and model evaluation [Matt (?)] | |
| + | * - Adaboost, boosting and bagging ... [?] | ||
| − | ==== Knowledge representation ==== | + | ==== Knowledge representation and Learning ==== |
---- | ---- | ||
* - ontologies ... | * - ontologies ... | ||
| − | ==== Text analysis ==== | + | ==== Text analysis and learning ==== |
---- | ---- | ||
| − | *- text analysis, classification, relevance feedback, latent semantic [Ahmed] | + | * - text analysis, classification, relevance feedback, latent semantic [Ahmed] |
| − | ==== Image analysis | + | ==== Image analysis and learning ==== |
| − | + | * - Image analysis and learning | |
| − | + | ||
| − | + | ||
| − | * - | + | |
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Revision as of 18:15, 26 January 2009
Contents
Introduction to the MLC a.k.a. the Machine Learning Curse
- + Machine learning introduction, paradigm, estimation ... [Matt]
Supervised Learning
Good old stuff from stistics
- - logistic regression and linear regression [Bernardo (?)]
- + LDA [Rossella (?) + Simone (?)]
Good old stuf from machine learning
- + Decision trees [Matt]
- ~ Decision rules [Matt + Andrea (?)]
- - Association rules [?]
Statistical approaches
- + Bayesian classifiers [Matt]
Non parametric methods
- - KNN & friends, Locally weighted regression,
Kernel methods
- ~ SVM and Kernel methods [Matt (?) + Luigi (?) + (?)]
Unsupervised Learning
Distributions of data
- + Feature selection and projection (PCA, ICA, ...) [Rossella (?)+ Simone]
Clustering
- + Clustering galore [Davide (?)]
Graphical models
- ~ Bayesian networks [Matt + Luigi (?)]
Non Parametric techniques
- Kernel based density estimation, ... [Rossella (?) + Luigi (?) + Simone]
Time Varying Models
- + Markov Chains [Matt + Bernardo (?) +Simone]
- + Hidden Markov Models [Matt + Simone (?)]
- - Dynamic Bayesian Networks
Learning Techniques and Methodologies
- ~ Cross-Validation, Bootstrapping, and model evaluation [Matt (?)]
- - Adaboost, boosting and bagging ... [?]
Knowledge representation and Learning
- - ontologies ...
Text analysis and learning
- - text analysis, classification, relevance feedback, latent semantic [Ahmed]
Image analysis and learning
- - Image analysis and learning
