Lecture

Dimensionality Reduction: PCA & Autoencoders

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In course

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

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented. ...

Description

This lecture covers the concepts of Principal Component Analysis (PCA) and Autoencoders for dimensionality reduction. Starting with the basics of PCA, it explains how to find the most important signal while removing noise. It then delves into Kernel PCA for nonlinear data. The discussion extends to Autoencoders, emphasizing their nonlinear mappings and applications in denoising and sparsity. The lecture also explores Convolutional Autoencoders and their role in generating new samples. Lastly, it showcases the practical use of Autoencoders for image retrieval and data generation.

Instructor

Mathieu Salzmann

I am a Senior Researcher at EPFL-CVLab, and, since May 2020, an Artificial Intelligence Engineer at ClearSpace (50%). Previously, I was a Senior Researcher and Research Leader in NICTA's computer vision research group. Prior to this, from Sept. 2010 to Jan 2012, I was a Research Assistant Professor at TTI-Chicago, and, from Feb. 2009 to Aug. 2010, a postdoctoral fellow at ICSI and EECS at UC Berkeley under the supervision of Prof. Trevor Darrell. I obtained my PhD in Jan. 2009 from EPFL under the supervision of Prof. Pascal Fua.

Official source

Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

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