Basic introduction to state of the art data analysis and automated classification.
Basic introduction to state of the rt data analysis and automated classification.
Frontal lectures in classroom
Modalities may change if requirtement by objective conditions (COVID 19)
Live lecture in presence (whenver possible)
Interactive labs.
Data visualization, descriptive statistics
regression and correlation. Logistic regression.
Bayes approach to learning. MAP.
TS, CS, trainign and generalization error. Confuson matrix. ROC.
LDA, SVM.
Kernel trick: non linear SVM
PCA, non linear techniques for dimension reduction
K-nn
CART.
Clustering: k-means and hierarchical clustering.
Ensblem techniques. Boosting
Data visualization, descriptive statistics
regression and correlation. Logistic regression.
Bayes approach to learning. MAP.
TS, CS, trainign and generalization error. Confuson matrix. ROC.
LDA, SVM.
Kernel trick: non linear SVM
PCA, non linear techniques for dimension reduction
K-nn
CART.
Clustering: k-means and hierarchical clustering.
Ensblem techniques. Boosting
a) Chapters from: Pattern Recognition and Machine Learning (Information Science and Statistics) Bishop C.M: Editore: Springer, 2007
b) Chapters from:Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython (Inglese) W.Mckinney O'reilly 2017
Tutorial on line, handsout
Teacher's handouts
Chapters from:Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython (Inglese) W.Mckinney O'reilly 2017
Jupyter notebooks