RES.9-003 | Summer 2015 | Graduate

Brains, Minds and Machines Summer Course


Tutorial 3. Machine Learning

Tutorial Overview

An array of handwritten digits accompanied by a 3-D scatter plot. Visualization of the results of principal components analysis applied to high-dimensional data capturing visual properties of handwritten digits. The data was reduced to three dimensions that capture most of the variation in the original data, roughly segregating the data into the corresponding digits, as portrayed by the different colors of the data points. (Image courtesy of Lorenzo Rosasco, used with permission.)

A key aspect of intelligent systems is their ability to learn from data or past experience. Modern methods for data analysis also draw heavily on techniques for learning patterns in data. This tutorial introduces many common methods for machine learning that are used in the fields of intelligence science and data science. The video lectures explore some of the basic concepts and theory underlying the behavior of various learning methods and their application to different kinds of problems. This theory is complemented by hands-on computer labs in the MATLAB® computing environment, to explore the behavior of machine learning methods in practice.

Unit Activities

Useful Background

  • Introductions to calculus, linear algebra, probability and statistics
  • Introduction to computer programming and MATLAB (see our MATLAB Tutorial)

Videos and Slides

Machine Learning Lab Exercises

This website for the Machine Learning Day was prepared by Lorenzo Rosasco and Georgios Evangelopoulos for the 2016 Brains, Minds, and Machines summer course. It contains descriptions of lab activities related to the machine learning methods presented in the above tutorial videos, with supporting MATLAB code and data files that can be downloaded from the website.

Further Study

This tutorial is based in part on the MIT course 9.520 Statistical Learning Theory and Applications. Materials from the MIT course 6.867 Machine Learning, taught by Tommi Jaakkola, are available on MIT OpenCourseWare. Free online courses on machine learning are also available through edX (search for “machine learning”).

Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2007. ISBN: 9780387310732.

Rosasco, L. “Introductory Machine Learning Notes.” (PDF) (2016).

Hastle, T., R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition (Springer Series in Statistics). Springer, 2009. ISBN: 9780387848570. [Preview with Google Books]

Course Info

As Taught In
Summer 2015
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