2018/2019

2018/2019

DM270

ING-INF/06 (ELECTRONIC AND INFORMATION BIOENGINEERING)

DEPARTMENT OF ELECTRICAL,COMPUTER AND BIOMEDICAL ENGINEERING

BIOENGINEERING

PERCORSO COMUNE

1°

2nd semester (06/03/2019 - 14/06/2019)

6

50 lesson hours

Italian

WRITTEN AND ORAL TEST

BELLAZZI RICCARDO (titolare) - 6 ECTS

Basic knowledge of statistics and probability theory. Basic knowledge of informatics and statistical software tools

The course aims to provide students with methodological skills and techniques to: * use in biomedical applications a large class of algorithms that are able to learn decision rules from data and automatically improve their performance based on experience.The student at the end of the course, should be able to: * soundly apply data mining approaches to learn decision rules from data * use machine learning software tools and statistical packages The course will include both lectures and practical hands-on computer lessons.

Learning decision rules - supervised learning
Introduction: Machine Learning and Data Mining in the biomedical sciences.
Areas of application of automatic methods for classification: diagnosis, prognosis, research
The basic concepts: examples, instances, attributes, and representation of decision rules
Decision Trees: learning techniques for pruning
Bayesian methods: Naive Bayes discriminant analysis
Regression models: linear model, logistic regression, neural networks, support-vector machines
Method and k-nearest distance measures
Learning of rules covering methods, beam-search methods
Techniques of feature selection. Information gain and Relief
Evaluation of learning algorithms and problems of evaluation in the biomedical field
Training and Testing. Accuracy, calibration, sensitivity and specificity, precision and recall, F measure
Methods for performance evaluation. Cross Validation, Bootstrap and ROC curves.
Unsupervised learning
Association Rules
Clustering methods: K-means, K-medoids, hierarchical clustering, self-organizing maps
Evaluation of the results of the clustering methods
Applications of data mining in bio-medicine: diagnosis, prognosis, classification, functional genomics
Practical Activities
The CRISP methodology for data mining in bio-medicine.
Hands-on with computer programs: Orange, Weka and Matlab for the solution of classification problems.

Lectures (hours/year in lecture theatre): 40
Practical class (hours/year in lecture theatre): 10
Practicals / Workshops (hours/year in lecture theatre): 0

T. Mitchell. Machine Learning. Mc Graw Hill.

P. Tan, M. Steinbach, V. Kumar. Introduction to data mining. Addison Wesley.

I. Witten, E. Frank. Data mining. Morgan Kaufmann.

R. Bellazzi. Slides.

Learning decision rules: A written exam and an essay about a data mining problem to be carried on with machine learning software on a data set provided to the students.

Learning decision rules: A written exam and an essay about a data mining problem to be carried on with machine learning software on a data set provided to the students.