61 lines
2.6 KiB
Org Mode
61 lines
2.6 KiB
Org Mode
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* Programma AA 2018/2019 - 6 crediti
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- Introduction OK
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- Differences between tasks and models OK
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- Binary classification and transformation of a binary classification model into a multiple class model OK
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- Concept learning by means of logical formulas, OK
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- Tree models and their purposes, OK
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- Rule models, OK
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- Subgroup discovery OK
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- Linear models (least squares, regression), perceptron, OK
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- Support Vector Machines and Kernel methods. OK
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- Bias-variance decomposition ?? OK ?? capitoli prima
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- The problem of overfitting. ?? OK ?? capitoli prima
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- Models based on distance, such as k-nearest neighbors OK
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- K-means clustering OK
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- Hierarchical clustering, OK
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- Density based clustering (DBSCAN) OK
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- Validation and verification of the results on data (5-cross validation? Fuffa) OK
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- Ensemble learning (not in official program) OK
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Experiments with a real system on data sets: Scikit Learn
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/Note: chap_12 experiments not asked at the exam but might be useful for scikit/
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* Diff 9 / 6 crediti
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- Distinctions between types of features, the allowed operations and statistical descriptions, MAGARI LO FAMO LO STESSO EH??
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- Transformation among features (normalization, discretization, calibration, etc). MAGARI LO FAMO LO STESSO EH??
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- Probabilistic models (such as maximum likelihood estimation, logistic regression, Bayes models and naive Bayes, Expectation-Maximization). NONONO.jpg
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- Evaluation of the models and of the statistical significance tests on the results. PFFFF.jpg
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* Programma AA 2018/2019 - 9 crediti
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- Introduction
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- Differences between tasks and models
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- Binary classification and transformation of a binary classification model into a multiple class model,
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- Concept learning by means of logical formulas,
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- Tree models and their purposes,
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- Rule models,
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- Subgroup discovery,
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- Linear models (least squares, regression), perceptron,
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- Support Vector Machines and Kernel methods.
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- Bias-variance decomposition
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- The problem of overfitting.
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- Models based on distance, such as k-nearest neighbors
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- K-means clustering
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- Hierarchical clustering,
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- Density based clustering.
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- Distinctions between types of features, the allowed operations and statistical descriptions,
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- Transformation among features (normalization, discretization, calibration, etc).
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- Probabilistic models (such as maximum likelihood estimation, logistic regression, Bayes models and naive Bayes, Expectation-Maximization).
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- Evaluation of the models and of the statistical significance tests on the results.
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- Validation and verification of the results on data
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- Experiments with a real system on data sets: Scikit Learn
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