## Most downloaded documents in introduction to machine learning

# Support Vector Machines-Introduction to Machine Learning-Lecture 11-Computer Science

Support Vector Machines, Andreas Argyriou, Large Margin Classification, Optimal Separating Hyperplane, Optimal Linear Classifier, Representer Theorem, Regularization, Lagrange Multipliers, Max-Margin Optimization, Quadratic Programming, Margin Dec...

# Tutorial on Probability and Estimation-Introduction to Machine Learning-Lecture 01-Computer Science

Tutorial on Probability and Estimation, Dhruv Batra, Probability, Continuous Random Variables, Bias, Probabilistic Model, Probability Distributions, Sequence Probability, Parameter Estimation Problem, Maximum Likelihood Estimator, Bernoulli, Bayes...

# Introduction to Machine Learning-Lecture 24-Computer Science

Feature Selection, Multilayer Networks, Minimum-Residual Projection, PCA, Compression, Classification, Gaussians, Probabilistic, Linear Subspaces, Unsupervised Learning, Feature Selection, Filter Methods, Mutual Information, Max-MI Feature Selecti...

# Multilayer Networks-Introduction to Machine Learning-Lecture 25-Computer Science

Multilayer Networks, Advanced Topics, Feed-Forward Networks, Training, Backpropagation, MLP, Universal Approximators, Model Complexity, Sequential Data, Markov Models, General Graphical Models, Undirected Models, Semi-Supervised Learning, Active L...

# Tutorial on Probability and Estimation-Introduction to Machine Learning-Lecture 02-Computer Science

Tutorial on Probability and Estimation, Bernoulli Distribution, MAP Estimates, Beta Conjugate Prior, Multivariate Gaussian Distributions, Gaussians, Central Limit Theorem, Univariate Gaussian Distribution, Moments, Multivariate Gaussian, Matrix, M...