Study-unit DATA SCIENCE AND APPLICATIONS IN PHYSICS
| Course name | Physics |
|---|---|
| Study-unit Code | A002331 |
| Location | PERUGIA |
| Curriculum | Fisica della materia |
| Lecturer | Livio Fano' |
| Lecturers |
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| Hours |
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| CFU | 6 |
| Course Regulation | Coorte 2023 |
| Supplied | 2023/24 |
| Supplied other course regulation | |
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Sector | FIS/04 |
| Type of study-unit | Opzionale (Optional) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | Italian |
| Contents | Introduction to statistical learning and to the most common tools |
| Reference texts | The Elements of Statistical Learning (Data Mining, Inference, and Prediction) Autors: Trevor Hastie Robert Tibshirani Jerome Friedman |
| Educational objectives | Learning from Data with statistical and computational tools for big and complex data. Specific applications to Physics. |
| Prerequisites | "Statistical Methods for Data Analysis" is suggested. |
| Teaching methods | Classroom lessons and practice. |
| Other information | Data Science combines advanced statistical and computational methods, with specific infrastructural solutions at high scalability and high performances. |
| Learning verification modality | Students will be requested to: 1) during the course: provide a presentation to the classroom based on one of the arguments discussed during the first half of the study program 2) end of the course: provide a written report on an assigned argument 3) oral test |
| Extended program | Introduction to Statistical Learning: 1) Prediction accuracy, model preparation and supervised learning 2) Regression and Classification 3) Model selection 4) Decision Trees - random forest 5) Support Vector Machine 6) Unsupervised Learning and Principal Component Analysis 7) Neural Network and Deep Learning |