Overview
This course covers various unsupervised learning techniques such as Gaussian Mixture Models, clustering with K-means and Hierarchical methods, Principal Component Analysis (PCA), Matrix Factorization for movie recommendations, Latent Dirichlet Allocation, Restricted Boltzmann Machines (RBM), Singular Value Decomposition (SVD) for image compression, Denoising and Variational Autoencoders, and Generative Adversarial Networks (GANs). The course aims to teach learners how to apply these tools to analyze and extract patterns from data without labeled outcomes. The intended audience for this course includes individuals interested in machine learning, data analysis, and artificial intelligence.
Syllabus
Gaussian Mixture Models.
Clustering: K-means and Hierarchical.
Principal Component Analysis (PCA).
How does Netflix recommend movies? Matrix Factorization.
Latent Dirichlet Allocation (Part 1 of 2).
Training Latent Dirichlet Allocation: Gibbs Sampling (Part 2 of 2).
Restricted Boltzmann Machines (RBM) - A friendly introduction.
Singular Value Decomposition (SVD) and Image Compression.
Denoising and Variational Autoencoders.
A Friendly Introduction to Generative Adversarial Networks (GANs).
Taught by
Serrano.Academy