BMM Summer Course 2019: Machine Learning Day

Machine Learning Day

Marine Biological Laboratory, Woods Hole, MA

Tues. 13 Aug. 2019
Theory: Morning (9:15 - 2:45), Lillie Auditorium
Practice/Labs: Afternoon (3:00 - 6:00), Loeb 306

BMM Summer Course 2017 Machine Learning Day website
BMM Summer Course 2018 Machine Learning Day website

Instructors: Lorenzo Rosasco (lrosasco at, Sasha Rakhlin (
Universita' di Genova, Istituto Italiano di Tecnologia, MIT
TAs: Andrzej Banburski (, Pouya Bashivan (, Kohitij Kar ( and Qianli Liao (

Course Description

Learning, its principles and computational implementations, is at the very core of intelligence. Machine Learning (ML) is the key to developing intelligent systems and analyzing data in science and engineering. ML engines enable intelligent technologies such as Alexa, Siri, Cortana, Google Now, Watson, AplhaGo, or self driving cars, to name a few. At the same time ML methods help make sense of the flood of online or biological data, forming the basis of a new Science of Data.

This one day course provides an introduction to essential concepts and algorithms at the core of ML. Theory classes in the morning are complemented by hands-on lab sessions in the afternoon.

(Optional): Instead of working on Matlab, you can go through the python course (download files ZIP)


Time Session   Material
9:00 Theory Local Methods, Bias-Variance and Model Selection slides
  Theory Regularization: Linear and Kernel Least Squares see above
  Theory How to Learn with Streaming Data  
3:00 (Optional) MATLAB Warm-Up: Data Generation code
  Practice k-NN and Cross-validation code
  Practice Regularized Least Squares (RLS) code
  Practice Kernel RLS code/data
  Practice PCA and Orthogonal Matching Pursuit (OMP) code
  (Optional) Learning with Real Data code/data


Basic Probability, Calculus, Linear Algebra


  • L. Rosasco, Introductory Machine Learning Notes, Draft, 2016 (pdf).
  • T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, 2nd Ed., 2009 (pdf available on authors' website).

Further reading

  • T. Poggio and S. Smale, Mathematics of Learning: Dealing with Data, Notices of the AMS, 2003 (pdf).
  • P. Domingos, A few useful things to know about Machine Learning, Comm. ACM, 55 (10), 2012 (pdf).

Related courses

  • BMM SS 2015 - Videos of the ML class in BMM summer course 2015.
  • MLCC - ML crash course (10 lectures).
  • ISML2, Unige - Undergraduate intro to ML (term-long).
  • RegML 2018 - Advanced ML course (20 lectures).
  • MIT 9.520/6.860 - Statistical Learning Theory and Applications, Fall 2016 (term-long).