Methods and Languages for Data Management
A.Y. 2021/2022
Learning objectives
The aim of the course is the introduction of methods and techniques for describing, summarizing and finding a structure in a data set, with particular attention to cultural heritage datasets.
Expected learning outcomes
Students will know how to perform explorative analysis, some basic inferences and the most common statistical tests using a statistical analysis software. Moreover, they will know the main techniques of machine learning both for regression and classification problems, and will be aware of the main machine learning issues.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
Parte 1 - descriptive analysis methods: frequency tables, charts, poition indices, dispesion indices, hetherogeneity indices. Some case studies using SPSS.
Parte 2 -statistical inference methods: estimators of the mean and of the variance, confidence intervals, hypothesis tests. Some case studies using SPSS.
Parte 3 - ontroduction to machine learning: supervised and non supervised methods; classification methods (clustering, decision trees, dendrograms, logistic regression), prevision methods (linear regression, support vector machines, neural networks); important issues about machine learning (overfitting, non linearity, dimensionality reduction, unbalanced data); measure of performance (accuracy, confusion table, specificity, sensitivity).
Parte 2 -statistical inference methods: estimators of the mean and of the variance, confidence intervals, hypothesis tests. Some case studies using SPSS.
Parte 3 - ontroduction to machine learning: supervised and non supervised methods; classification methods (clustering, decision trees, dendrograms, logistic regression), prevision methods (linear regression, support vector machines, neural networks); important issues about machine learning (overfitting, non linearity, dimensionality reduction, unbalanced data); measure of performance (accuracy, confusion table, specificity, sensitivity).
Prerequisites for admission
none
Teaching methods
General computer science, Probabilistic and statistic methods
Teaching Resources
nessuno
Assessment methods and Criteria
The final exam consists of a practical part (a simple case study analysis using a software for data analysis) followed by an oral part regarding topics covered in class.
INF/01 - INFORMATICS - University credits: 6
Laboratories: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Zanaboni Anna Maria
Professor(s)
Reception:
Wednesday 10:30-12:30 -- by appointment
via Celoria 18, 5th floor