Partners
MLCC is made possible by the SLING Project, funded by a Consolidator ERC Grant, by the contributions of the DIBRIS and DIMA Departments of UniGe. This PhD course is part of the ELLIS Genoa and MIT’s Center for Brains, Minds & Machines (CBMM) activities.
At a glance
The Machine Learning Crash Course (MLCC) provides an introduction to the fundamental concepts and algorithms of Machine Learning. The course is suitable for undergraduate/graduate students, as well as professionals. It is open to students from any University upon selection.
Attendance will be exclusively in person, and the course will not be streamed online.
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Machine Learning is key to develop intelligent systems and analyze data in science and engineering. Machine Learning systems enable intelligent technologies such as Siri, Google self-driving car, or Chat-GPT to name a few. At the same time, Machine Learning methods help deciphering the information in our DNA and make sense of the flood of information gathered on the web, forming the basis of a new “Science of Data”. This course introduces the fundamental methods at the core of modern Machine Learning. It covers theoretical foundations as well as essential algorithms.
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The university credits granted for attendance of the course and/or successful completion of the test are established by the Director of your specific course of study, depending on your university/course criteria.
For those who want to take the final test, it will consist in completing remotely the notebooks that the class will work on during the labs, and writing a report commenting on the numerical results obtained. Active attendance will be part of the evaluation. -
The course is self contained but assumes basic knowledge in calculus and probability.
Logistics
The school will be held in our beautiful Genova, in the DIBRIS/DIMA building in Via Dodecaneso 35, 16146, home of MaLGa Center.
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The course will be held in the DIBRIS Department building of Via Dodecaneso 35, Genova. Indications to the classroom will be visible at the entrance. You will have access to vending machines during the breaks betweein classes. We suggest bringing your own laptop, but please keep into consideration that, due to the number of expected participants, your battery should be fully charged.
We will have a 1.5-hour lunch break every day: although there is a bar close to the department, you are encouraged to bring your own lunch and enjoy the break with your peers. There are common areas just out of the classroom where you will be able to sit and have lunch.
Speakers & instructors
Classes will be held by members of the SLING Project team and MaLGa Center faculty members from UniGe’s DIBRIS and DIMA Departments.
Programme
Classes on theoretical and algorithmic aspects will be complemented by practical lab sessions.
Tuesday, June 25, 2024
Class
09:30-11:00 - Class 1 - Introduction to Statistical Machine Learning
Lorenzo Rosasco
Class
11:30-13:00 - Class 2 - Local Methods and Model Selection
Lorenzo Rosasco
Lab
14:30-16:30 - Lab 1 - Local Methods for Classification
Wednesday, June 26, 2024
Class
09:30-11:00 - Class 3 - Empirical Risk Minimization with Linear Models
Silvia Villa
Class
11:30-13:00 - Class 4 - Optimization and SGD
Silvia Villa
Lab
14:30-16:30 - Lab 2 - ERM with Linear Models
Thursday, June 27, 2024
Class
09:30-11:00 - Class 5 - Kernel Methods
Simone Di Marino
Class
11:30-13:00 - Class 6 - Neural Networks
Simone Di Marino
Lab
14:30-16:30 - Lab 3 - Kernel Methods and Neural Networks
Friday, June 28, 2024
Class
09:00-10:00 - Class 7 - Sparsity and variable selection
Matteo Santacesaria
Class
10:30-11:30 - Class 8 - Dimensionality Reduction and PCA
Matteo Santacesaria
Lab
12:00-13:30 - Lab 4 - Sparsity and PCA
Seminar
15:00-17:00 - TBC
How to apply
Applications are open until midnight on the 1st of April; notifications of acceptance will be sent by the 8th of April.
Submit application
Monday, April 1, 2024
Registration Fees
Upon acceptance of your application, you will be requested to pay a registration fee, depending on your role and affiliation.
| Role | Fee |
|---|---|
| UniGe and IIT affiliates | waived |
| Non-UniGe students and postdocs | €50 |
| Non-UniGe professors | €100 |
| Professionals | €300 |
Organization
An introduction to Machine Learning
Only for information which is not available online, please refer to MaLGa’s Lab Manager.