Lectures in course CS-233(a): Introduction to machine learning (BA3)

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

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

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

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

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

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.