Machine Learning
2017/18

Instructors:

Alessandro Verri (alessandro.verri [at] unige.it)
Lorenzo Rosasco (lorenzo.rosasco [at] unige.it)

TA:

Fabio Anselmi (anselmi [at] mit.edu)
Raffaello Camoriano (raffaello.camoriano [at] iit.it)
Luigi Carratino (luigi.carratino [at] dibris.unige.it)

Class Times:

Mon: 11-13am; Wed: 9-11am; Fri: 9-11am; Exceptions: See syllabus

Location:

DIBRIS-room 216; DIBRIS-lab Software 2 (SW2), 3rd floor

Office Hours:

Thu 2-3pm, office 320

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 name a few. At the same time machine learning methods help unlocking 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 for supervised and unsupervised learning. Classes on theoretical and algorithmic aspects are complemented by practical lab sessions.

More info: http://computerscience.dibris.unige.it/course/ml/

2016/17 edition: mit.lcsl.edu/courses/ml/1617/index.html

Prerequisites

The mathematical tools needed for the course will be covered in some classes in the 2 weeks that precede the course.

Grading

9 CFU course (standard):

< 9 CFU course:

Note 1: The complexity of the oral discussion will be proportional to the number of points.

Note 2: People not attending labs are required to submit one report per lab proving and discussing the results of that lab.

Syllabus

Some of the later classes may be subject to reordering or rescheduling.

Date

Title

Lecturer

Resources

Class 1

Mon 16 Oct

Introduction

AV

Class 2

Wed 18 Oct

Statistical Learning Theory

AV

Class 3

Fri 20 Oct

Lab Octave/Matlab goal: basic matlab/octave+ data generation

AV, FA, LC

Class 4

Mon 23 Oct

Local Methods

AV

Class 5

Wed 25 Oct

Bias Variance Trade-Off

AV

Class 6

Fri 27 Oct

Lab on LM: K-NN, PW for classification

AV, FA, LC

Class 7

Mon 30 Oct

Least Squares Regression

AV

Class 8

Tue 31 Oct

(9:00am - 11:00am
OR 4:00pm - 6:00pm)

Least Squares Classification

AV

Class 9

Fri 3 Nov

Lab LS/LDA

LR, FA, LC

Class 10

Mon 6 Nov

Feature Maps

LR

Class 11

Wed 8 Nov

Kernels

LR

Class 12

Fri 10 Nov

Lab Kernels

LR, FA, LC

Class 13

Mon 13 Nov

Regularization Networks and Representer Theorem

LR

Class 14

Wed 15 Nov

Logistic Regression & Support Vector Machines

LR

Class 15

Fri 17 Nov

Lab Loss functions

LR, FA, LC

Class 16

Mon 20 Nov

Dimensionality Reduction

LR

Class 17

Wed 22 Nov

Variable Selection & Sparsity

LR

Class 18

Fri 24 Nov

Lab Dimensionality Reduction and Variable Selection

LR, FA, LC

Class 19

Mon 27 Nov

Density and support estimation

LR

Class 20

Wed 29 Nov

Clustering & K-Means

LR

Class 21

Fri 1 Dec

Lab Clustering

LR, FA, LC

Class 22

Wed 6 Dec

Bayesian ML

AV

Class 23

Mon 11 Dec

Graph Regularization

LR

Class 24

Wed 13 Dec

Multitask Learning

LR

Class 25+26

Thu 14 Dec

(2:00pm - 6:00pm)

Double LabLearning Pipeline - time is (2pm - 6pm)

LR, FA, LC

Class 27

Mon 18 Dec

Neural Networks

LR

References