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– Welcome to class

– Training methods revisited

– Architectural methods

– 1. PCA

– Q&A on Definitions: Labels, unconditional, and un, selfsupervised learning

– 2. Auto-encoder with Bottleneck

– 3. K-Means

– 4. Gaussian mixture model

– Regularized EBM

– Yann out of context

– Q&A on Norms and Posterior: when the student is thinking too far ahead

– 1. Unconditional regularized latent variable EBM: Sparse coding

– Sparse modeling on MNIST & natural patches

– 2. Amortized inference

– ISTA algorithm & RNN Encoder

– 3. Convolutional sparce coding

– 4. Video prediction: very briefly

– 5. VAE: an intuitive interpretation

– Helpful whiteboard stuff

– Another interpretation

Description:

Explore advanced machine learning techniques in this comprehensive lecture by Yann LeCun. Dive into Principal Component Analysis (PCA), Auto-encoders, K-means clustering, Gaussian mixture models, sparse coding, and Variational Autoencoders (VAE). Learn about training methods, architectural approaches, and regularized Energy-Based Models (EBM). Gain insights into unconditional regularized latent variable EBMs, amortized inference, convolutional sparse coding, and video prediction. Benefit from in-depth Q&A sessions on labels, supervised learning, norms, and posterior distributions. Enhance your understanding with practical examples using MNIST and natural patches, and explore intuitive interpretations of VAEs.

PCA, AE, K-Means, Gaussian Mixture Model, Sparse Coding, and Intuitive VAE

Alfredo Canziani

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#Computer Science #Machine Learning #Deep Learning #K-Means Clustering #Gaussian Mixture Models