Target

Duration

24 hours

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

When

Jun 27 2022 , 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.

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