Lectures related to Principal Component Analysis: Dimension Reduction

Principal Component Analysis: Geometric Interpretation and Dimension Reduction

Explores Principal Component Analysis for dimension reduction and data representation in a new basis.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Principal Component Analysis: Dimensionality Reduction

Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

Unsupervised Learning: Dimensionality Reduction

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

Introduction to Machine Learning: Linear Models

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

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Principal Component Analysis: Eigenfaces

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Dimensionality Reduction: PCA & Autoencoders

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Clustering: Theory and Practice

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

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Introduction to Machine Learning: Supervised Learning

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

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA

Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.

Neural Networks Recap: Activation Functions

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

Clustering & Density Estimation

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.