Dimension Reduction: Curse of Dimensionality

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Unsupervised Learning: Dimensionality Reduction and Clustering

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

Understanding Autoencoders

Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.

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.

Landscape and Generalisation in Deep Learning

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

Principal Component Analysis: Olympic Medals & Image Compression

Explores PCA for predicting medals distribution and compressing face images.

Nonlinear Dimension Reduction Methods

Explores nonlinear dimension reduction methods preserving neighborhoods in lower-dimensional spaces.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Dimensionality Reduction: PCA & Autoencoders

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

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Data Visualization: Principles and Techniques

Covers principles and techniques of data visualization, emphasizing storytelling, anomaly detection, and effective communication of results.