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Instructors: |
Lorenzo Rosasco (lorenzo.rosasco@mit.edu) |
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TA: |
Alessandro Rudi (ale_rudi@mit.edu), Raffaello Camoriano (raffaello.camoriano@iit.it) |
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Class Times: |
Tuesday: 11:00 - 13:00. Wednesday: 9:00 - 11:00. Thursday: 11:00 - 13:00. From 24th Feb 2015 to 23th Apr 2015 |
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Location: |
DIBRIS-aula 711(Tuesday,Wednesday) DIBRIS-SWII (Thursday), |
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Office Hours: |
Wednesday, Office 207 |
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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 |
Resources |
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Class 1 |
Tue 24 Feb |
Intro |
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Class 2 |
Wed 25 Feb |
Statistical Learning Theory |
Scribe 2 |
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Class 3 |
Thu 26 Feb |
Lab Octave/Matlab goal: basic matlab/octave+ data generation |
Lab 3 |
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Class 4 |
Tue 3 Mar |
Local Methods |
Scribe 4 |
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Class 5 |
Wed 4 Mar |
Bias Variance Trade-Off |
Scribe 5 |
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Class 6 |
Thu 5 Mar |
Lab on LM: K-NN, PW for classification |
Lab 6 |
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Class 7 |
Tue 10 Mar |
Least Squares Regression |
Scribe 7 |
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Class 8 |
Wed 11 Mar |
Least Squares Classification |
Scribe 8 |
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Class 9 |
Thu 12 Mar |
Lab LS/LDA |
Lab 9 |
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Class 10 |
Tue 17 Mar |
Feature Maps |
Scribe 10-11 |
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Class 11 |
Wed 18 Mar |
Kernels |
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Class 12 |
Thu 19 Mar |
Lab Kernels |
Lab 12 |
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Class 13 |
Tue 24 Mar |
Regularization Networks and Representer Theorem |
Scribe 13 |
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Class 14 |
Wed 25 Mar |
Logistic Regression & Support Vector Machines |
Scribe 14A Scribe 14B |
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Class 15 |
Thu 26 Mar |
Lab Loss functions |
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Tue 31 Mar |
no class |
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Class 16-17 |
Wed 1 Apr |
Double Lab Learning Pipeline- time is 9:30-13 |
Lab 17 |
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2-9 Apr |
no class |
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Class 18 |
Tue 14 Apr |
Dimensionality Reduction |
Scribe 18 |
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Class 19 |
Wed 15 Apr |
Variable Selection & Sparsity |
Scribe 20 |
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Class 20 |
Thu 16 Apr |
Lab Dimensionality Reduction and Variable Selection |
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Class 21 |
Tue 21 Apr |
Clustering & K-Means |
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Class 22 |
Wed 22 Apr |
Machine Learning: To the infinity...and beyond! |
Scribe 22 |
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Class 23 |
Thu 23 Apr |
Projects Presentations |
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Class 24 |
Fri 27 Feb |
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).