Concept

Linear discriminant analysis

Related lectures (31)

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Linear Dimensionality Reduction: PCA and LDA

Explores PCA and LDA for linear dimensionality reduction in data, emphasizing clustering and class separation techniques.

Clustering: Theory and Practice

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

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Clustering: K-means & LDA

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

Supervised Learning: Classification Algorithms

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

Kernel Regression

Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.

Linear Discriminant Analysis: Generative Methods

Covers Linear Discriminant Analysis (LDA) as a generative method for classification.

Linear Classification: Logistic Regression

Covers linear classification using logistic regression, regularization, and multiclass classification.

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Linear Classification: Signed Distance and Perceptron

Explores signed distance, perceptron, logistic regression, cross entropy, and multi-class classification.

Dimensionality Reduction: PCA & LDA

Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Canonical Correlation Analysis: Linear and Kernel CCA

Introduces Canonical Correlation Analysis for finding joint features in separate datasets, extending to multimodal data and non-linear features.

Linear Systems: Modeling and Identification

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Clustering: Unsupervised Learning

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

Discriminant Analysis: Bayes Rule

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Linear Classification: Variants and Stochastic Gradient Descent

Explores the variants of gradient descent, with a focus on stochastic gradient descent and linear classification.

Supervised Learning Essentials

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.