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Dec 23 2017

Learning From Data MOOC – The Lectures

#machine #learning #and #big #data



  • Taught by Feynman Prize winner Professor Yaser Abu-Mostafa.
  • The fundamental concepts and techniques are explained in detail. The focus of the lectures is real understanding. not just “knowing.”
  • Lectures use incremental viewgraphs (2853 in total) to simulate the pace of blackboard teaching.
  • The 18 lectures (below) are available on different platforms:

    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.

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