En la web del Instituto de Tecnología de California podemos encontrar un serie de vídeos orientados a la enseñanza de Machine Learning, titulado Learning from Data. Técnica ampliamente usada en el campo de la inteligencia artificial, dónde básicamente nuestra aplicación estudia el entorno en el que se encuentra para aprender más sobre el mismo y así poder tomar decisiones.
El índice de los vídeos es el siguiente:
Bayesian Learning Validity of the Bayesian approach (prior, posterior, unknown versus probabilistic) Bias-Variance Tradeoff Basic derivation (overfit and underfit, approximation-generalization tradeoff) Example (sinusoidal target function) Noisy case (Bias-variance-noise decomposition) Bin Model Hoeffding Inequality (law of large numbers, sample, PAC) Relation to learning (from bin to hypothesis, training data) Multiple bins (finite hypothesis set, learning: search for green sample) Union Bound (uniform inequality, M factor) Data Snooping Definition and analysis (data contamination, model selection) Ensemble Learning Overview of aggregation methods (boosting, blending, before and after the fact) Error Measures User-specified error function (pointwise error, CIA, supermarket) Gradient Descent Basic method (Batch GD) (first-order optimization) Discussion (initialization, termination, local minima, second-order methods) Stochastic Gradient Descent (the algorithm, SGD in action) Initialization - Neural Networks (random weights, perfect symmetry) Learning Curves Definition and illustration (complex models versus simple models) Linear Regression example (learning curves for noisy linear target) Learning Diagram Components of learning (target function, hypothesis set, learning algorithm) Input probability distribution (unknown distribution, bin, Hoeffding) Error measure (role in learning algorithm) Noisy targets (target distribution) Where the VC analysis fits (affected blocks in learning diagram) Learning Paradigms Types of learning (supervised, reinforcement, unsupervised, clustering) Other paradigms (review, active learning, online learning) Linear Classification The Perceptron (linearly separable data, PLA) Pocket algorithm (non-separable data, comparison with PLA) Linear Regression The algorithm (real-valued function, mean-squared error, pseudo-inverse) Generalization behavior (learning curves for linear regression) Logistic Regression The model (soft threshold, sigmoid, probability estimation) Cross entropy error (maximum likelihood) The algorithm (gradient descent) Netflix Competition Movie rating (singular value decomposition, essence of machine learning) Applying SGD (stochastic gradient descent, SVD factors) Neural Networks Biological inspiration (limits of inspiration) Multilayer perceptrons (the model and its power and limitations) Neural Network model (feedforward layers, soft threshold) Backpropagation algorithm (SGD, delta rule) Hidden layers (interpretation) Regularization (weight decay, weight elimination, early stopping) Nonlinear Transformation Basic method (linearity in the parameters, Z space) Illustration (non-separable data, quadratic transform) Generalization behavior (VC dimension of a nonlinear transform) Occam's Razor Definition and analysis (definition of complexity, why simpler is better) Overfitting The phenomenon (fitting the noise) A detailed experiment (Legendre polynomials, types of noise) Deterministic noise (target complexity, stochastic noise) Radial Basis Functions Basic RBF model (exact interpolation, nearest neighbor) K Centers (Lloyd's algorithm, unsupervised learning, pseudo-inverse) RBF network (neural networks, local versus global, EM algorithm) Relation to other techniques (SVM kernel, regularization) Regularization Introduction (putting the brakes, function approximation) Formal derivation (Legendre polynomials, soft-order constraint, augmented error) Weight decay (Tikhonov, smoothness, neural networks) Augmented error (proxy for out-of-sample error, choosing a regularizer) Regularization parameter (deterministic noise, stochastic noise) Sampling Bias Definition and analysis (Truman versus Dewey, matching the distributions) Support Vector Machines SVM basic model (hard margin, constrained optimization) The solution (KKT conditions, Lagrange, dual problem, quadratic programming) Soft margin (non-separable data, slack variables) Nonlinear transform (Z space, support vector pre-images) Kernel methods (generalized inner product, Mercer's condition, RBF kernel) Validation Introduction (validation versus regularization, optimistic bias) Model selection (data contamination, validation set versus test set) Cross Validation (leave-one-out, 10-fold cross validation) VC Dimension Growth function (dichotomies, Hoeffding Inequality) Examples (growth function for simple hypothesis sets) Break points (polynomial growth functions, puzzle) Bounding the growth function (mathematical induction, polynomial bound) Definition of VC Dimension (shattering, distribution-free, Vapnik-Chervonenkis) VC Dimension of Perceptrons (number of parameters, lower and upper bounds) Interpreting the VC Dimension (degrees of freedom, Number of examples) Además cada vídeo está acompañado de las diapositivas que el profesor usa, así como los deberes asignados en el mismo.
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