Physics Data Analysis

Period: Second semester

Course unit contents:

* Gradient descent methods
* Ridge and LASSO regularization
* Deep neural networks and convolutional version
* Clustering
* Data visualization
* Energy-based models
* Restricted Boltzmann machines
* Combination of models: bagging, random forests, boosting, XGBoost

Planned learning activities and teaching methods: This course aims to expose the students to modern tools for classifying data and machine learning techniques so that they can apply those methods in lab experiences with computers. The first half of the course (24 hours) is reserved to learn general principles via applications. In contrast, the second half of the course allows the students, in small groups, to develop a deeper understanding of one specific subject by carrying out a small project.

The first half of the course includes theoretical explanations of some key procedures for data analysis or a class of algorithms, followed by exercise sessions in which the students will apply the new ideas on computers. This learning through practical experience is expected to improve understanding of theoretical tools. The numerical analysis includes adopting and modifying pre-built software or sketching simple algorithms from scratch.

At the end of the course, the student is expected to be able to: define the different machine learning methods used in the course; apply the most suitable method to solve a particular problem; identify key aspects of the procedure used and develop a combination of methods to improve the solution to the problem; judge the result obtained and explain and justify the use of particular methods.