OVERVIEW

The course intends to provide the basic notions for data and signal analysis with emphasis on application to biology and medicine.

AIMS AND CONTENT

LEARNING OUTCOMES

The course provides students with the essential tools and operational skills for quantitative analysis of data and signals of interest for medicine and biology, on a probabilistic perspective

AIMS AND LEARNING OUTCOMES

By the end of the course the students will be able to:

1. Design and apply methods of analysis and modeling of data - including temporal data (signals) - of interest for medicine and biology

2. Identify the correct approach (model selection, model identification, data visualization) for a specific data analysis problem

3. Use MATLAB to display and model  biomedical data and signals 

PREREQUISITES

There are no formal prerequisites, but  the course requires solid foundations in calculus and linear algebra.

TEACHING METHODS

The course is organized as a combination of lectures and practical activities. 

Lectures will focus on theory and methods for data analysis. 

Practical activities will focus on application of theory to real data analysis problems in the context of bioengineering.

SYLLABUS/CONTENT

A. Data Analysis and Data Display. Data types. Descriptive statistics. Analysis as modeling. Statistical data analysis. Regression. Visual display of information.

B. Probability density estimates. Unsupervised learning. Gaussian model. Principal Component Analysis, Factor Analysis, Independent Component Analysis, Cluster analysis and the EM algorithm. Graphical models. Regression and factor analysis as graphical models. 

C. Pattern analysis and decision theory. Bayesian decision theory. Bayes classifiers. Logistic classifiers. Performance of a classifier: ROC curve. Generalized linear models. Introduction to neural networks. Model generalization and bias-variance trade-off.

D. Model selection. Statistical Inference. Hypothesis testing. General Linear models and the analysis of variance. Mixed-effects models. Bayesian model selection.

E. Dynamic Models. Temporal data (signals). Hidden Markov Models, Linear dynamical systems. Kalman filters.

RECOMMENDED READING/BIBLIOGRAPHY

There is no course textbook; the course material draws from a variety of sources.

Recommended references: 

1. Murphy, KP (2022) Probabilistic Machine Learning: An Introduction. MIT Press.

2. Bishop, CM (2006) Pattern Recognition and Machine Learning. Springer

3. Barber D (2006) Machine Learning A Probabilistic Approach.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/11159/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written exam (weight 50%)

Project work (individuals or couples, weight 50%)

• Solution of a real problem of biomedical data analysis/processing, chosen from a list of proposed projects
• Development of software for calculation/analysis/processing
• Interactive application (MATLAB Livescript) reporting the results
• Fixed deadline for submission (early February)
• Oral discussion of activity and results

ASSESSMENT METHODS

Project work will be assessed in terms of:

1) Documentation (correctess, clarity, synthesis, terminology)

2) Implementation (code structure and organization, efficiency)

3) Data Visualization (technical quality of figures, adequacy of display technique, efficacy, clarity)

4) Bonus (max  2 pts) if the report provides additional analysis (in addition to those required). Bonus is only awarded if maximum score is obtained in the other three criteria.

FURTHER INFORMATION

Ask the teacher for other information not included here