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: 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: lcsl.mit.edu

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

	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

• 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