MLC
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MLC: Machine Learning Curse (pun intended!)
- + Machine learning introduction, paradigm, estimation ... [Matt]
Contents
Supervised
Distributions of data
- + Feature selection and projection (PCA, LDA, ICA, ...) [Rossella (?)+ Simone]
Linear & other
- - Good old stuff: logistic regression, linear regression [Bernardo (?)]
- + LDA
Rules
- + Decision trees [Matt]
- ~ Decision rules [Matt + Andrea (?)]
- - Association rules [?]
Bayes
- + Bayesian classifiers [Matt]
Non parametric methods
- - KNN & friends, Locally weighted regression,
...?
~ SVM and Kernel methods [Matt (?) + Luigi (?) + (?)]
Statistical Learning & Unsupervised
Graphical models
- + Markov Chains [Matt + Bernardo (?) +Simone]
- ~ Bayesian networks [Matt + Luigi (?)]
- (Dynamic Bayesian Networks) + Hidden Markov Models [Matt + Simone (?)]
Expectation & maximization
- Kernel based density estimation, ... [Rossella (?) + Luigi (?) + Simone]
- Clustering
Knowledge representation
- - ontologies ...
Text analysis
- - text analysis, classification, relevance feedback, latent semantic [Ahmed]
Image analysis
Unmapped:
- - Adaboost, boosting and bagging ... [?]
- ~ Cross-Validation, Bootstrapping, and model evaluation [Matt (?)]
- + Clustering galore [Davide (?)]
indexing, ...
