Instructors

Lorenzo Rosasco
UniGe | MaLGa & DIBRIS - lorenzo.rosasco@unige.it
Giovanni S. Alberti
UniGe | MaLGa & DIMA - giovanni.alberti@unige.it
Simone Di Marino
UniGe | MaLGa & DIMA - simone.dimarino@unige.it

Duration

24 hours

When

From Jun 27 2022 to Jul 1 2022

Where

UniGe | DIBRIS, Via Dodecaneso 35, 16146 Genova, Room 506

Important Dates

Application deadline: April 30th [applications will be reviewed after this date if the max number has not been reached]
Payment deadline: June 15th
Notifications of acceptance: by May 15th
The school will be held in person

Abstract

Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. Machine Learning engines enable intelligent technologies such as Siri, Kinect or Google self driving car, 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 provides an introduction to the fundamental methods at the core of modern Machine Learning. It covers theoretical foundations as well as essential algorithms. Classes on theoretical and algorithmic aspects are complemented by practical lab sessions.

This introductory course is suitable for undergraduate/graduate students, as well as professionals.

0. Mon - 08:30-09:30 - Registration
   1. Mon - 9.30-11.00 - Class 1: - Introduction to Statistical Machine Learning
   2. Mon - 11.30-13.00 - Class 2: - Local Methods and Model Selection
   3. Mon - 14.30-16.30 - Lab 1: - Local Methods for Classification
   4. Tue - 9.30-11.00 - Class 3: - Empirical Risk Minimization with Linear Models
   5. Tue - 11.30-13.00 - Class 4: - Optimization and SGD
   6. Tue - 14.30-16.30 - Lab 2: - ERM with Linear Models
   7. Wed - 9.30-13:00 - Workshops - S. Villa, “Implicit regularization”; A. Seminara, “A tour of reinforcement learning”
   7. Thu - 9.30-11.00 - Class 5: - Kernel Methods
   8. Thu - 11.30-13.00 - Class 6: - Neural Networks
   9. Thu - 14.30-16.30 - Lab 3: - Kernel Methods and Neural Networks
   10. Fri - 9.30-11.00 - Class 7: Sparsity and variable selection
   11. Fri - 11.30-13.00 - Class 8: Dimensionality Reduction and PCA

Silvia Villa, “Implicit Regularization”
UniGe | MaLGa @ DIMA


A. Seminara, “A tour of reinforcement learning”
UniGe | MaLGa @ DICCA

Once accepted, each candidate has to follow the instructions in the acceptance email and proceed with the payment. The registration fee is non-refundable.

Fees
Non-UniGe students and postdocs: EUR 50
professors: EUR 100
professionals, in person: EUR 400
UniGe students and postdocs: free

To apply, complete the application form

Giulia Casu
UniGe | MaLGa & DIBRIS - giulia.casu@ext.unige.it

MIT 9.520 Statistical Learning Theory and Applications. This is a term long course of roughly
25 lectures offered to graduate students at MIT

Machine Learning 2018/2019. Undergraduate term-long introductory Machine Learning course offered at the University of Genova

CBMM Summer School: Machine Learning Classes. One day introduction to the essential concepts and algorithms at the core of modern Machine Learning

RegML master page. All editions of RegML. The course started in 2008 has seen an increasing national and international attendance over the years, with a peak of over 100 participants

MLCC master page. Previous editions of RegML. The course started in 2008 has seen an increasing national and international attendance over the years, with a peak of over 100 participants