AT1 - Data Science
Data science: context and goals.
Data science process.
Course organization and planning.
AT2 - Data Exploration
Basic concepts: data, records, attributes and information.
KDD process: from data to information.
AT3 - Data Visualization
Data description: granularity, distribution, dimensionality and sparsity.
AT4 - Classification
Classification. Notion of Concept. KNN. Accuracy. Data normalization and distance measures.
AT5 - Bayesian learning
Classification: MAP and Naive Bayes.
Evaluation: training strategies.
AT6 - Data Balancing and Evaluation
Data balancing: resampling and SMOTE.
Evaluation: measures and ROC charts.
Basics on missing imputation.
AT7 - Decision tree learning
Classification: decision trees - algorithms, measures.
AT8 - Overfitting
Overfitting and the Occam's razor.
AT9 - Ensemble classification
Classification ensembles: random forests and xGBoost.
AT10 - Connectionist and evolutionary learning
Classification: Neural Networks and Genetic algorithms - introduction and motivation.
AT11 - Pattern mining
Pattern Mining. Apriori algorithm. Evaluation. Discretization methods.
AT12 - Sequential pattern mining
Pattern Mining: other approaches. Sequential Pattern Mining.
AT13 - Clustering
AT14 - High-dimensional data analysis
AT15 - Feature selection and extraction
AT16 - Biclustering
AT17 - Regression
Multiple linear regression.
Lazy and tree-based regression.
Evaluation of regression models.
AT18 - Anomaly detection
Outlier analysis: approaches and applications.
AT19 - Network data analysis
Network data analysis.
AT20 - Social and web data analysis
SNA: HITS and PageRank algorithms.
AT21 - Time series representations
AT22 - Temporal data mining
Mining (multivariate) time series.
Mining event and interval data.
Temporal pattern mining, classification and regression.
AT23 - Time series forecasting
AT24 - Big data
AT25 - Privacy and ethical concerns
AutoML and ethical concerns.
AT26 - Closing