esercizi naz

anno3/apprendimento_automatico/programma.org

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