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.
|9:00||Theory||Local Methods, Bias-Variance and Model Selection||slides|
|Theory||Regularization: Linear and Kernel Least Squares||slides|
|Theory||Variable Selection and Dimensionality Reduction||slides|
|1:30||(Optional)||MATLAB Warm-Up: Data Generation||code|
|Practice||k-NN and Cross-validation||code|
|Practice||Regularized Least Squares (RLS)||code|
|Practice||PCA and Orthogonal Matching Pursuit (OMP)||code|
|(Optional)||Learning with Real Data||code/data|
Basic Probability, Calculus, Linear Algebra