# 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...

# 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...

# 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...

# 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...

# 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...

# 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...

# Support Vector Machines-Introduction to Machine Learning-Lecture 12-Computer Science

Support Vector Machines, Andreas Argyriou, SVM, Nonlinear Classification, Kernel, SVM Classification, Non-Separable Case, Slack Variables, Regularization, Loss, Nonlinear Features, Nonlinear Features, Logistic Regression, Nonlinear Mapping, Dot Pr...

# 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...

# 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...

# Clustering-Introduction to Machine Learning-Lecture 23-Computer Science

Clustering, Spectral Clustering, Random Walk Model, Random Walk, Transition Matrix Decomposition, Eigende Composition, Finite Number, Beyond Binary Clustering, Unsupervised Learning, Principal Component Analysis, Dimensionality Reduction, Linear S...

# Clustering-Introduction to Machine Learning-Lecture 22-Computer Science

Clustering, Setting, Gaussians, Gaussians EM, K-Means, Clusters, K-Medoids Clustering, Hierarchical Structure, Bottom-Up Agglomeration, Agglomerative Hierarchical Clustering, Linkage Schemes, Hierarchical Clustering, Divisive Clustering, Greg Shak...

# LWR-Introduction to Machine Learning-Lecture 21-Computer Science

LWR, Unsuperwised Learning, Kernel Density Estimate, Nearest Neighbor, Kernel Regression, Parametric, Locally Weighted Regression, Linear LWR, Complexity, Nearest Neighbor Search, Locality Sensitive Hashing, LSH, Hash Functions, Unsupervised Learn...