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):

• attending classes: 5 points participation, 8 points quiz, 10 points project, 10 points oral discussion
• not attending classes: 10 points project, 23 points oral discussion
  
  

< 9 CFU course:

• attending classes: 5 points participation, 8 points quiz, 20 points oral discussion
• not attending classes: 33 points oral discussion

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

• L. Rosasco. Introductory Machine Learning Notes.

• If you have suggestions or find any typos in the notes please fill this form:https://goo.gl/forms/OPJ9Ggk1aWYObYCB3

• Hastie, Tibshirani and Friedman. Elements of statistical learning.

• Larry Wasserman. Clustering chapter