MLCC 2014

Machine Learning Crash Course

Course organized by the POLITECMED cluster of Companies and Research Institutions in collaboration with
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS),
- Center for Brain Minds and Machines (CBMM),
and with the contribution of the European Regional Development Fund within the POR 2007-2013 of Regione Liguria.

Dates and registration

The course will be held on February 18th-21th, 2014 at DIBRIS (University of Genoa, Italy)

Registration for the course is now closed.

We reached the number of required registration so the course will take place.
See you @MLCC_2014!

Course at a Glance

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.

L�apprendimento automatico sta emergendo come un campo fondamentale per lo sviluppo di sistemi intelligenti e l�analisi di dati nelle scienze naturali e in ingegneria. Sistemi basati sull�apprendimento automatico sono alla base di tecnologie intelligenti come Siri, Kinect o le macchine che si guidano da sole sviluppate da Google. Allo stesso tempo, le tecniche di apprendimento automatico costituiscono la base di una nuova �Scienza dei Dati�, e sono ad esempio di aiuto nel decifrare l�informazione contenuta nel DNA umano o nell'ordinare il flusso incessante di informazioni proveniente da Internet. Questo corso fornisce un'introduzione alle tecniche fondamentali che formano il nucleo dell�apprendimento automatico moderno. Sono trattate sia le basi teoriche, sia gli algoritmi fondamentali dell�apprendimento automatico. Le lezioni di carattere teorico sono complementate da sessioni pratiche in laboratorio.

Il corso � adatto a laureandi, laureati e professionisti del settore.

Instructors

Lorenzo Rosasco -- University of Genova (also Istituto Italiano di Tecnologia and Massachusetts Institute of Technology) lorenzo (dot) rosasco (at) unige (dot) it

Francesca Odone -- University of Genova, francesca (dot) odone (at) unige (dot) it

Venue

The course will be held at the Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) of the University of Genova in
Via Dodecaneso 35, 16146 Genova.
When looking for directions keep this address in mind, since DIBRIS has multiple locations (not so close to one another). Here you can find directions and travelling information.
Here is a list of places where you can go for lunch. And here is a link to the online Google map.

Syllabus

Class #	Day	Date	Month	Daily Schedule	Subject
1	Tue	18	February	9:00 - 11:00	Introduction to Machine Learning
2	Tue	18	February	11:00 - 1:00	Local Methods and Model Selection
3	Tue	18	February	14:00 - 16:00	*Lab* on LM: K-NN, PW for classification
4	Wed	19	February	9:00 - 11:00	Regularization Networks I: Linear Models
5	Wed	19	February	11:00 - 1:00	Regularization Networks II: Kernels
6	Wed	19	February	14:00 - 16:00	*Lab* on Regularization Networks
7	Thu	20	February	9:00 - 11:00	Dimensionality Reduction and PCA
8	Thu	20	February	11:00 - 1:00	Variable Selection and Sparsity
9	Thu	20	February	14:00 - 16:00	*Lab* PCA and Sparsity
10	Fri	21	February	9:00 - 11:00	Applications of Machine Learning

Classroom

(Changed!) Room 710, 7th floor DIBRIS, via Dodecaneso 35 , Genova (see here for directions).

Credits and Exam (optional)

A certificate attendance is given after the course.

Prerequisites

The course makes use of basic notions and tools from calculus, linear algebra and probability.

Reading list and useful links

References:

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Prediction, Inference and Data Mining. Springer Verlag, 2009

Further readings:
T. Poggio and S. Smale. The Mathematics of Learning: Dealing with Data. Notices of the AMS, 2003

Useful Links:
MIT 9.520: Statistical Learning Theory and Applications, Fall 2013 (http://www.mit.edu/~9.520/).
Stanford CS229 Machine Learning Autumn 2013 (http://cs229.stanford.edu). See also the Coursera version (https://www.coursera.org/course/ml).