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Instructors: |
Lorenzo Rosasco (lorenzo.rosasco@unige.it) |
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TA: |
Alessandro Rudi (alessandro.rudi@iit.it) |
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Class Times: |
Tuesday: 11:00 - 13:00 |
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Location: |
DIBRIS-aula 711 (Tuesday,Wednesday); DIBRIS-SWII (Thursday), |
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Office Hours: |
Tuesday, 14:00 - 16:00 |
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.
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Date |
Title (Lecturer) |
Resources |
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Class 1 |
Wed 24 Feb |
Intro (LR) |
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Class 2 |
Wed 24 Feb |
Statistical Learning Theory (LR) |
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Class 3 |
Thu 25 Feb |
Lab Octave/Matlab goal: basic matlab/octave+ data generation (LR, AR, RC) |
Files |
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Class 4 |
Tue 1 Mar |
Local Methods (LR) |
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Class 5 |
Wed 2 Mar |
Bias Variance Trade-Off (AR) |
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Class 6 |
Thu 3 Mar |
Lab on LM: K-NN, PW for classification (AR, RC) |
Files |
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Class 7 |
Tue 8 Mar |
Least Squares Regression (LR) |
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Class 8 |
Wed 9 Mar |
Least Squares Classification (AR) |
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Class 9 |
Thu 10 Mar |
Lab LS/LDA (AR, RC) |
Files |
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Class 10 |
Tue 15 Mar |
Feature Maps (LR) |
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Class 11 |
Wed 16 Mar |
Kernels (LR) |
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Class 12 |
Thu 17 Mar |
Lab Kernels (LR, AR, RC) |
Files |
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Class 13 |
Tue 22 Mar |
Regularization Networks and Representer Theorem (LR) |
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Class 14 |
Wed 23 Mar |
Logistic Regression & Support Vector Machines (LR) |
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Tue 29 - Wed 30 Mar |
No class |
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Class 16+17 |
Thu 31 Mar |
Double Lab Learning Pipeline - time is 14:00-18:00 (LR, AR, RC) |
Files |
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Class 18 |
Tue 5 Apr |
Dimensionality Reduction (LR) |
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Class 15 |
Tue 5 Apr |
Lab Loss functions - time is 14:00-16:00 (LR, AR, RC) |
Files |
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Class 19 |
Wed 6 Apr |
Variable Selection & Sparsity (LR) |
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Class 20 |
Thu 7 Apr |
Lab Dimensionality Reduction and Variable Selection (LR, AR, RC) |
Files |
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Class 21 |
Tue 12 Apr |
Clustering & K-Means (LR) |
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Class 22 |
Wed 13 Apr |
Machine Learning: To the infinity...and beyond! (LR) |
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Class 23 |
Thu 14 Apr |
Projects Presentations |
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Class 24 |
Tue 1 Mar, 14:00 - 16:00, Room 215 |
Math Camp (AR) |
L. Rosasco. Introductory Machine Learning Notes.
T. Poggio and S. Smale. The Mathematics of Learning: Dealing with Data. Notices of the AMS, 2003
Pedro Domingos. A few useful things to know about machine learning. Communications of the ACM CACM Homepage archive. Volume 55 Issue 10, October 2012 Pages 78-87.
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Prediction, Inference and Data Mining. Second Edition, Springer Verlag, 2009 (available for free from the author's website).
MIT 9.520: Statistical Learning Theory and Applications, Fall 2013 (http://www.mit.edu/~9.520/).
Stanford CS229 Machine Learning Autumn 2013 (http://cs229.stanford.edu). See also the Coursera version (https://www.coursera.org/course/ml).