Machine Learning
2018/19

Instructors:

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

TA:

Fabio Anselmi (anselmi [at] mit.edu)
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:

Wed 2-3pm room 323

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/

2017/18 edition: http://lcsl.mit.edu/courses/ml/1718/index.html

Link to Aulaweb: https://2018.aulaweb.unige.it/course/view.php?id=1783

Link to LCSL website: http://lcsl.mit.edu/#/home

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

Date

Title

Lecturer

Resources

Class 1

Mon 8 Oct

Intro statistics

AV

Class 2

Wed 10 Oct

Statistical Learning Theory

AV

Class 3

Fri 12 Oct

Lab Octave/Matlab goal: basic matlab/octave (data generation) 

AV, FA, LC

Class 4

Mon 15 Oct

Local Methods

AV

Class 5

Wed 17 Oct

Bias Variance Trade-Off

AV

Class 6

Fri 19 Oct

Lab on LM: K-NN, PW for classification

AV, FA, LC

Class 7

Mon 22 Oct

Least Squares Regression

LR

Class 8

Wed 17 Oct

Least Squares Classification

LR

Class 9

Fri 26 Oct

Lab LS/LDA

LR, FA, LC

Class 10

Mon 29 Oct

Feature Maps

LR

Class 11

Wed 31 Oct

Kernels + Lab kernels (afternoon)

LR

Class 12

Fri 2 Nov

No Class

Class 13

Mon 5 Nov

Regularization Networks and Representer Theorem

LR

Class 14

Wed 7 Nov

Logistic Regression & Support Vector Machines 

LR

Class 15

Fri 9 Nov

Lab Loss functions

LR

Class 16

Mon 12 Nov

Dimensionality Reduction

LR

Class 17

Wed 14 Nov

Variable Selection & Sparsity

LR

Class 18

Fri 16 Nov

Lab Dimensionality Reduction and Variable Selection

LR, FA, LC

Class 19

Mon 19 Nov

Density and support estimation

LR

Class 20

Wed 21 Nov

Clustering & K-Means

LR

Class 21

Fri 23 Nov

Lab Clustering

LR, FA, LC

Class 22

Mon 26 Nov

Bayesian ML

LR

Class 23

Wed 28 Nov

Graph Regularization

LR

Class 24

Mon 3 Dic

Multitask Learning

LR

Class 25+26

Fri 7 Dic


Double Lab Learning Pipeline - time is (2pm - 6pm)

LR, FA, LC

Class 27

Mon 10 Dic

Neural Networks

LR

References