ISML 2: Machine Learning, Spring 2016


Lorenzo Rosasco (


Alessandro Rudi (
Raffaello Camoriano (

Class Times:

Tuesday: 11:00 - 13:00
Wednesday: 9:00 - 11:00
Thursday: 14:00 - 16:00
From 23rd Feb 2016 to 21st Apr 2016


DIBRIS-aula 711 (Tuesday,Wednesday); DIBRIS-SWII (Thursday),

Office Hours:

Tuesday, 14:00 - 16:00

Course description

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 namea few. At the same time machine learning methods help deciphering the information in our DNA and make sense of the flood of informationgathered on the web, forming the basis of a new Science of Data. This course provides an introduction to the fundamental methods atthe core of modern machine learning. It covers theoretical foundations as well as essential algorithms for supervised andunsupervised learning. Classes on theoretical and algorithmic aspects are complemented by practical lab sessions.


The mathematical tools needed for the course will be covered in class,and include material form the following courses: Geometria (25882); Elementi di Statistica e Probabilità (67083), Calcolo Numerico (61804), Probabilità1(52205).


Requirements for grading (other than attending lectures) are: attendance to classes + labs, project +discussion.


Follow the link for each class to find a detailed description, suggested readings, and class slides. Some of the later classes may be subject to reordering or rescheduling.


Title (Lecturer)


Class 1

Wed 24 Feb

Intro (LR)

Class 2

Wed 24 Feb

Statistical Learning Theory (LR)

Class 3

Thu 25 Feb

Lab Octave/Matlab goal: basic matlab/octave+ data generation (LR, AR, RC)


Class 4

Tue 1 Mar

Local Methods (LR)

Class 5

Wed 2 Mar

Bias Variance Trade-Off (AR)

Class 6

Thu 3 Mar

Lab on LM: K-NN, PW for classification (AR, RC)


Class 7

Tue 8 Mar

Least Squares Regression (LR)

Class 8

Wed 9 Mar

Least Squares Classification (AR)

Class 9

Thu 10 Mar



Class 10

Tue 15 Mar

Feature Maps (LR)

Class 11

Wed 16 Mar

Kernels (LR)

Class 12

Thu 17 Mar

Lab Kernels (LR, AR, RC)


Class 13

Tue 22 Mar

Regularization Networks and Representer Theorem (LR)

Class 14

Wed 23 Mar

Logistic Regression & Support Vector Machines (LR)

Tue 29 - Wed 30 Mar

No class

Class 16+17

Thu 31 Mar

Double Lab Learning Pipeline - time is 14:00-18:00 (LR, AR, RC)


Class 18

Tue 5 Apr

Dimensionality Reduction (LR)

Class 15

Tue 5 Apr

Lab Loss functions - time is 14:00-16:00 (LR, AR, RC)


Class 19

Wed 6 Apr

Variable Selection & Sparsity (LR)

Class 20

Thu 7 Apr

Lab Dimensionality Reduction and Variable Selection (LR, AR, RC)


Class 21

Tue 12 Apr

Clustering & K-Means (LR)

Class 22

Wed 13 Apr

Machine Learning: To the infinity...and beyond! (LR)

Class 23

Thu 14 Apr

Projects Presentations

Class 24

Tue 1 Mar, 14:00 - 16:00, Room 215

Math Camp (AR)


Further readings

Useful Links