- Machine Learning Video Library (Caltech)
- 연구
- 2014.03.20 15:01

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- Aggregation
- Overview of ensemble learning (boosting, blending, before and after the fact)

- 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)

- 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)
- 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)

http://work.caltech.edu/library/

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