EE-566: Adaptation and learning

About
Privacy
Disclaimer

Graph Chatbot

Lectures in this course (28)

Bayesian Inference: Part 2

Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

Regularized Cross-Entropy Risk

Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.

Linear Regression: Mean-square-error Inference

Covers the MSE problem in linear regression models, focusing on the optimal estimator and data fusion methods.

Maximum Likelihood: Estimation and Inference

Introduces maximum likelihood estimation, discussing its properties and applications in statistical analysis.

Recursive Least-Squares: Weighted Formulation

Covers the Recursive Least-Squares algorithm with weighted formulation for real-time data updating.

Regularization: Promoting Optimal Solutions

Covers regularization in least-squares problems, promoting optimal solutions while addressing challenges like non-uniqueness, ill-conditioning, and over-fitting.

L1 Regularization: Sparse Solutions and Dimensionality Reduction

Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Page 2 of 2