|SCIENTIFIC DISCIPLINARY SECTOR||ING-INF/05|
In the information age any system or device generates some form of data for diagnostic purposes or analysis.
he course details the techniques for analyzing data in order to extract useful information and knowledge for decision making.
Students will be provided with advanced skills related to machine learning and data analysis.
Students will learn insights on machine learning and data analysis methodologies and a series of real world applications.
The student will be able to apply the acquired skills to a case study by deriving the model of the phenomenon that generated the data under analysis.
Coding (Matlab/Python/R), linear algebra, probability and statistics.
The course consists of lectures and practical lab sessions using Matlab/Python/R
C. C. Aggarwal "Data Mining - The textbook" 2015
T. Hastie, R.Tibshirani, J.Friedman "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009.
S. Shalev-Shwartz, S. Ben-David "Understanding machine learning: From theory to algorithms" 2014
I. Goodfellow, Y. Bengio, A. Courville "Deep learning" 2016
L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020
Office hours: By appointment.
Office hours: By appointment, scheduled by email.
Office hours: Contact the instructor by email.
LUCA ONETO (President)
DAVIDE ANGUITA (President Substitute)
All class schedules are posted on the EasyAcademy portal.
Oral by appointment.
Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.
The student will solve a real problem at will by applying the techniques learned during the course.