Linear discriminant analysis

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Explores Kernel Ridge Regression, equivalent formulations, Representer Theorem, Kernel trick, and predicting with kernels.

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Covers a mathematical introduction to deep learning, including challenges, power of linear classifiers, model scaling, and theoretical aspects.

Explores supervised learning through classification as a geometric problem and the concept of finding a separating surface.

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

Explores linear classification, feature extraction, loss functions, cross-validation, and spatial scales in signal processing and fMRI analysis.

Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Introduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.

Covers image classification challenges, machine learning concepts, linear regression, and nearest neighbor approach in autonomous vehicles.

Covers the Perceptron model, SGD, and Fisher's Linear Discriminant in binary classification.

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

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