Machine Learning Day
BMM Summer Course 2015
The Marine Biological Laboratory, Woods Hole, MA
Instructor: Lorenzo Rosasco (lrosasco at mit.edu)
Universita' di Genova, Istituto Italiano di Tecnologia, MIT
TAs: Carlo Ciliberto, Georgios Evangelopoulos ([cciliber, gevang] at mit.edu)
Wednesday 19 Aug. 2015
Theory: Morning (9 - 10:30, 11:00 - 12:30) | Practice/Labs: Afternoon (1:30 - 3:00, 3:30 - 5:00)
Location: Lillie Auditorium (Theory), Loeb 306 (Labs), MBL
Course Description
Machine Learning is the key to developing intelligent systems and analyzing data in science and engineering. Machine learning engines enable intelligent technologies such as Watson, Siri, Cortana, Google Now or self driving cars, to name a few. At the same time ML methods help decipher information in our DNA or make sense of the flood of online data, forming the basis of a new Science of Data.
This one day course provides an introduction to the essential concepts and algorithms at the core of modern Machine Learning. Theory classes in the morning are complemented by hands-on lab sessions in the afternoon.
Prerequisites
Basic Probability
Basic Calculus/Linear Algebra
Schedule
| Time | Session | Material | |
|---|---|---|---|
| 9:00 | Theory | Local Methods and Model Selection | slides |
| Theory | Regularization and Nonparametrics | slides | |
| Theory | Dimensionality Reduction and Variable Selection | slides | |
| 1:30 | (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) | Real Data and Challenge | code/data |
Links and courses
- MIT 9.520, Statistical Learning Theory and Applications, Fall 2014.
- Stanford CS229, Machine Learning, Autumn 2014.
- Coursera, Machine Learning by Stanford.