Machine Learning: Algorithms and their Applications is the second week of the Summer School in Coding, Data Collection and Mining.
Machine Learning: Algorithms and their Applications Summer School explores fundamental machine learning algorithms and their applications. These topics will also be explored in the first week of the Summer School entitled Introduction to computer programming in Python and, above all, during the Level I Master in Big Data and Management.
The Summer School is aimed at recent graduates — or those graduating soon — with a Bachelor’s or Master’s Degree in Economics, Statistics, Engineering, Information Systems, Mathematics or related fields.
- Classification Algorithms
- CART Trees and Random Forests
- Deep Learning
Schedule and Delivery
Lessons will be held from Monday to Friday from 9:30 to 17:30. For more information on, contact us by writing to email@example.com.
Except for legal provisions or regulations that prevent attending classes on-site, for which the School reserves the right to change the delivery method, classes will be held at the stated and scheduled locations.
Tuition and Funding
Tuition includes materials and access to all Luiss Business School facilities, but does not include accommodation.
Fee waivers are available for participation in more than one week.
For participants enrolling in the Master in Big Data and Management a.y. 2021-2022, the cost of the single week, will be deducted from the cost of the Master programme. Futher deductions and facilities are granted to those attending two or three Summer School weeks.
How to apply
In order to apply, candidates need to send a copy of their CV and ID document (or passport) along with the completed Enrolment Form via e-mail to firstname.lastname@example.org.
ECTS and Certificate of Participation
The Summer School Machine Learning: Algorithms and their Applications provides students with 4 ECTS - European Credit Transfer and Accumulation System.
At the end of the programme a certificate of participation will be issued to those who have attended at least 80% of the didactic activities included in the programme.