9.520: Statistical Learning Theory and Applications, Fall 2015

Class Times: Monday and Wednesday: 1:00pm - 2:30pm
Units: 3-0-9 H,G
Location: 46-3310

Tomaso Poggio (TP), Lorenzo Rosasco (LR).


Carlo Ciliberto, Georgios Evangelopoulos, Maximilian Nickel, Ben Deen, Hongyi Zhang, Stephen Voinea, Owen Lewis.

Office Hours: Friday 2-3 pm in 46-5156 (Poggio Lab lounge)
Email Contact : 9.520@mit.edu
Previous Class: FALL 2014
Further Info: 9.520 is currently NOT using the Stellar system
New: Videos available here!

Course description

The class covers foundations and recent advances of Machine Learning from the point of view of Statistical Learning Theory.

Understanding intelligence and how to replicate it in machines is arguably one of the greatest problems in science. Learning, its principles and computational implementations, is at the very core of intelligence. During the last decade, for the first time, we have been able to develop artificial intelligence systems that can solve complex tasks considered out of reach. ATM machines read checks, cameras recognize faces, smart phones understand your voice and cars can see and avoid obstacles.

The machine learning algorithms that are at the roots of these success stories are trained with labeled examples rather than programmed to solve a task. Among the approaches in modern machine learning, the course focuses on regularization techniques, that provide a theoretical foundation to high- dimensional supervised learning. Besides classic approaches such as Support Vector Machines, the course covers state of the art techniques exploiting data geometry (aka manifold learning), sparsity and a variety of algorithms for supervised learning (batch and online), feature selection, structured prediction and multitask learning. Concepts from optimization theory useful for machine learning are covered in some detail (first order methods, proximal/splitting techniques...).

The final part of the course will focus on deep learning networks. It will introduce a theoretical framework connecting the computations within the layers of deep learning networks to kernel machines. It will study an extension of the convolutional layers in order to deal with more general invariance properties and to learn them from implicitly supervised data. This theory of hierarchical architectures may explain how visual cortex learn, in an implicitly supervised way, data representation that can lower the sample complexity of a final supervised learning stage.

The goal of this class is to provide students with the theoretical knowledge and the basic intuitions needed to use and develop effective machine learning solutions to challenging problems.


We will make extensive use of linear algebra, basic functional analysis (we cover the essentials in class and during the math-camp), basic concepts in probability theory and concentration of measure (also covered in class and during the mathcamp). Students are expected to be familiar with MATLAB.


Requirements for grading (other than attending lectures) are: 2 problems sets, and a final project.

Problem Sets

Problem Set 1: Posted: Oct. 14, Due date extended to Mon, Nov. 02.
Problem Set 2. Posted: Nov. 12, Due date: Nov. 30.
Problem Set 2 submission process changed: please refer to the new instructions sent to the mailing list.


Project request: Fill the online form (by Nov. 25).

The course project can be any of the following: Tentative list of project topic examples (updated regularily): g-docs

Wikipedia articles (instructions)

You should use the standard Wikipedia article format, follow Wikipedia layout, style and content rules and create Sandbox pages for drafting and previewing your articles.


Nov. 18 (class 20): projects open/abstract submission (online form) (Nov. 18 - Nov. 25)
Nov. 25 (class 22): hard deadline
Dec. 16: final project submission


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.

Class Date Title Instructor(s)

Reading List

Notes covering the classes will be provided in the form of independent chapters of a book currently in draft format. Additional information will be given through the slides associated with classes (where applicable). The books/papers listed below are useful general reference reading, especially from the theoretical viewpoint. A list of suggested readings will also be provided separately for each class.

Book (draft)

Primary References

Background Mathematics References

Neuroscience Related References