#machine #learning #and #big #data
- Taught by Feynman Prize winner Professor Yaser Abu-Mostafa.
Here is the playlist on YouTube
Lectures are available on iTunes U course app
Place the mouse on a lecture title for a short description
The Learning Problem – Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem.
Is Learning Feasible? – Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample.
The Linear Model I – Linear classification and linear regression. Extending linear models through nonlinear transforms.
Error and Noise – The principled choice of error measures. What happens when the target we want to learn is noisy.
Training versus Testing – The difference between training and testing in mathematical terms. What makes a learning model able to generalize?
Theory of Generalization – How an infinite model can learn from a finite sample. The most important theoretical result in machine learning.
The VC Dimension – A measure of what it takes a model to learn. Relationship to the number of parameters and degrees of freedom.
Bias-Variance Tradeoff – Breaking down the learning performance into competing quantities. The learning curves.
The Linear Model II – More about linear models. Logistic regression, maximum likelihood, and gradient descent.
Neural Networks – A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers.
Overfitting – Fitting the data too well; fitting the noise. Deterministic noise versus stochastic noise.
Regularization – Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay.
Validation – Taking a peek out of sample. Model selection and data contamination. Cross validation.
Support Vector Machines – One of the most successful learning algorithms; getting a complex model at the price of a simple one.
Kernel Methods – Extending SVM to infinite-dimensional spaces using the kernel trick, and to non-separable data using soft margins.
Radial Basis Functions – An important learning model that connects several machine learning models and techniques.
Three Learning Principles – Major pitfalls for machine learning practitioners; Occam’s razor, sampling bias, and data snooping.
Epilogue – The map of machine learning. Brief views of Bayesian learning and aggregation methods.