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  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
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).