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

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

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.