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MGT-448: Statistical inference and machine learning

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Lectures in this course (47)

Decision Trees: Overfitting and Randomization

Explores decision trees, overfitting, and randomization in supervised learning, emphasizing the importance of managing variance and feature selection.

Supervised Learning: Decision Trees

Covers supervised learning with decision trees and feature selection for classification.

Addressing Overfitting in Decision Trees

Explores overfitting in decision trees and introduces random forests as a solution.

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Hoeffding's Inequality: Binomial Distribution

Explores Hoeffding's inequality and the binomial distribution, focusing on error minimization and generalization gaps in predictor selection.

Bayesian Parameter Estimation

Explores Bayesian Parameter Estimation, emphasizing cost minimization and reward maximization.

Bayesian Statistics: Regularization and Divergence

Covers Kullback-Leibler divergence, regularization, and Bayesian statistics to combat overfitting in machine learning models.

K-Means Clustering: Image Compression

Covers K-means algorithm for image compression and PCA for dimensionality reduction.

Eigenvalues and EM Algorithm

Covers eigenvectors, principal components, likelihood variables, EM algorithm, Jensen's inequality, and maximizing lower bounds.

Principal Component Analysis: Covariance Matrix and Eigenvalues

Explores Principal Component Analysis focusing on maximizing variance and finding eigenvalues.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

EM Algorithm: Lite-bulbs Lifetime Estimation

Explains the EM algorithm for estimating lite-bulbs lifetime and discusses its application to heavy-bulbs.

Bayesian Networks: Factorization and Sampling

Explains Bayesian Networks factorization and sampling methods using DAGs and Variable Elimination.

Variational Inference: Lower Bound and ELBO

Explains variational inference, Jensen's inequality, E-step, M-step, and MCMC sampling.

Exponential Family Distributions

Covers exponential family distributions, natural parameters, sufficient statistics, and logistic regression.

Graphical Models: Chain Rule and Variable Elimination

Covers the chain rule in graphical models and introduces variable elimination.

Neural Network Training

Covers the training process of a neural network, including feedforward, cost function, gradient checking, and visualization of hidden layers.

Structured Learning: Chau-Lieu Algorithm

Explores structured learning with the Chau-Lieu algorithm and delves into the basics of deep learning and neural networks.

Structure Learning: Chow-Liu Algorithm

Explores the Chow-Liu Algorithm for structure learning and optimizing distributions through spanning trees and K-L divergence.

Neural Networks: Supervised Learning and Backpropagation

Explains neural networks, supervised learning, and backpropagation for training and improving performance.

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