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.
MLCC 2015 is free of charge. We limited the number of accepted students to 120.
We reached the maximum number of students! Registration is now closed.
Classes will take place at the Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) of the University of Genova in Via Dodecaneso 35, 16146 Genova. See here for directions and travelling information
(new!) Morning classes (theory) will be held in classroom 506. Laboratories will take place in rooms SW1,SW2 and 218. Directions to the classrooms will be provided at the DIBRIS entrance in Via Dodecaneso 35.
Here you can find a list of hotels near the department (~ 20' walk) or in the city centre (~20' by bus).
Universita' di Genova
(also Istituto Italiano di Tecnologia and Massachusetts Institute of Technology)
lorenzo (dot) rosasco (at) unige (dot) it
Universita' di Genova
francesca (dot) odone (at) unige (dot) it
Universita' degli studi di Milano
Laboratoire Lagrange - CNRS, Observatoire de la Côte d'Azur and Université de Nice Sophia Antipolis
iCub Facility - Istituto Italiano di Tecnologia
Google Deep Mind
|1||Mon 22||9:30 - 11:00||Introduction to Machine Learning|
|2||Mon 22||11:30 - 13:00||Local Methods and Model Selection|
|3||Mon 22||14:00 - 16:00||Laboratory - Local Methods for Classification|
|4||Tue 23||9:30 - 11:00||Regularization Networks I: Linear Models|
|5||Tue 23||11:30 - 13:00||Regularization Networks II: Kernels|
|6||Tue 23||14:00 - 16:00||Laboratory - Regularization Networks|
|-||Wed 24||9:30 - 10:10||Talk by Cédric Févotte|
|-||Wed 24||10:20 - 11:00||Talk by Nicolò Cesa-Bianchi|
|-||Wed 24||11:30 - 12:10||Talk by Giorgio Metta|
|-||Wed 24||12:20 - 13:00||Talk by Joel Z Leibo|
|7||Thu 25||9:30 - 11:00||Dimensionality Reduction and PCA|
|8||Thu 25||11:30 - 13:00||Variable Selection and Sparsity|
|9||Thu 25||15:30 - 17:30||Laboratory - PCA and Sparsity|
|10||Fri 26||9:30 - 11:00||Clustering|
|11||Fri 26||11:30 - 12:15||Applications of Machine Learning|
|11||Fri 26||12:15 - 13:00||GURLS - Machine Learning made Easy|
|11||Fri 26||14:00 - 16:00||(Optional) Laboratory - Tutorial on GURLS|
Laboratory for Computational and Statistical Learning. Istituto Italiano di Tecnologia and Massachusetts Institute of Technology)
cciliber (at) mit (dot) edu
iCub Facility (also Laboratory for Computational and Statistical Learning) Istituto Italiano di Tecnologia
giulia (dot) pasquale (at) iit (dot) it