Automatic Understanding of the Visual World

Related lectures (31)

Page 3 of 4

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.