Lecturer: Riccardo Bellazzi  

Course name: Machine learning in biomedical problems
Course code: 502956
Degree course: Computer Engeneering
Disciplinary field of science: ING-INF/06
University credits: ECTS 6
Course website: n.d.

Specific course objectives

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 and use machine learning software tools and statistical packages. The course will include both lectures and practical hands-on computer lessons.

Course programme

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.

• 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.

Course entry requirements

Basic knowledge in statistics. Basic understanding of statistical software.

Course structure and teaching

Lectures (hours/year in lecture theatre): 45
Practical class (hours/year in lecture theatre): 0
Practicals / Workshops (hours/year in lecture theatre): 0

Suggested reading materials

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.

Testing and exams

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