Difference between revisions of "MLC"
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| − | ==== | + | ==== Introduction to the MLC a.k.a. the Machine Learning Curse ==== |
---- | ---- | ||
* + Machine learning introduction, paradigm, estimation ... [Matt] | * + Machine learning introduction, paradigm, estimation ... [Matt] | ||
| − | ==== | + | ==== Supervised Learning ==== |
| − | + | ---- | |
| − | ====== | + | ====== Good old stuff from stistics ====== |
| − | * - | + | * - logistic regression and linear regression [Bernardo (?)] |
| − | * + LDA | + | * + LDA [Rossella (?) + Simone (?)] |
| − | ====== | + | ====== Good old stuf from machine learning====== |
* + Decision trees [Matt] | * + Decision trees [Matt] | ||
* ~ Decision rules [Matt + Andrea (?)] | * ~ Decision rules [Matt + Andrea (?)] | ||
* - Association rules [?] | * - Association rules [?] | ||
| − | ====== | + | ====== Statistical approaches ====== |
* + Bayesian classifiers [Matt] | * + Bayesian classifiers [Matt] | ||
| Line 22: | Line 22: | ||
* - KNN & friends, Locally weighted regression, | * - KNN & friends, Locally weighted regression, | ||
| − | ====== | + | ====== Kernel methods ====== |
| − | ~ SVM and Kernel methods [Matt (?) + Luigi (?) + (?)] | + | * ~ 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 (?)] | * ~ Bayesian networks [Matt + Luigi (?)] | ||
| − | |||
| − | ====== | + | ======Non Parametric techniques====== |
* Kernel based density estimation, ... [Rossella (?) + Luigi (?) + Simone] | * 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 ==== | + | ==== 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 | |
| − | * - | + | |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
Latest revision as of 18:16, 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
