Unit DATA SCIENCE AND APPLICATIONS IN PHYSICS
- Course
- Physics
- Study-unit Code
- A002331
- Curriculum
- In all curricula
- Teacher
- Alessandro Rossi
- Teachers
-
- Alessandro Rossi
- Daniele Spiga
- Hours
- 21 ore - Alessandro Rossi
- 21 ore - Daniele Spiga
- CFU
- 6
- Course Regulation
- Coorte 2020
- Offered
- 2020/21
- Learning activities
- A scelta dello studente
- Area
- A scelta dello studente
- Academic discipline
- FIS/01
- 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