Factor Analysis Courses and Certifications

Simons Institute

Recent Progress in High-Dimensional Learning

Explore recent advancements in high-dimensional learning, covering factor analysis, phylogenetic reconstruction, and hidden Markov models, with applications in genetics and machine learning.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

MITCBMM

Learning What We Know and Knowing What We Learn - Gaussian Process Priors for Neural Data Analysis

Explore Gaussian process priors for neural data analysis, covering latent variable models, Bayesian inference, covariance kernels, and factor analysis. Gain insights into advanced techniques for understanding complex neural datasets.

• YouTube
• 1 hour 31 minutes
• Self-Paced
• Free Video

Graham Neubig

Neural Nets for NLP - Latent Variable Models

Explore latent variable models in NLP, covering variational autoencoders, discrete variables, and practical applications. Gain insights into generative modeling and advanced neural network techniques.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

Institute for Advanced Study

Latent Stochastic Differential Equations for Irregularly-Sampled Time Series - David Duvenaud

Explore advanced techniques for analyzing irregularly-sampled time series data using latent stochastic differential equations, with applications in predictive modeling and continuous-time machine learning.

• YouTube
• 1 hour 5 minutes
• Self-Paced
• Free Video

Institute for Advanced Study

Energy-Based Approaches to Representation Learning - Yann LeCun

Explore energy-based approaches to representation learning with Yann LeCun, covering self-supervised learning, video prediction, and sparse modeling in high-dimensional spaces.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

Association for Computing Machinery (ACM)

Recommender Systems - Beyond Machine Learning with Joe Konstan

Explore advanced recommender systems beyond machine learning, focusing on user behavior and experience. Learn about successes, failures, and innovative approaches to personalization and evaluation.

• YouTube
• 1 hour 11 minutes
• Self-Paced
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Tensor Decompositions and Their Applications

Explore advanced tensor decomposition techniques and their applications in latent variable models, phylogenetics, mixture models, and orbit retrieval with MIT's Ankur Moitra.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Tensor Decompositions and Their Applications

Explore tensor decompositions and their applications in latent variable models, focusing on Jennrich's algorithm and its use in various fields like phylogenetics and mixture models.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Simons Institute

Embedding as a Tool for Algorithm Design

Explore embedding algorithms for structured data prediction, combinatorial optimization, and dynamic network processes. Learn innovative approaches to algorithm design using latent variable models and mean field techniques.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

Alexander Amini

Deep Generative Modeling

Explore deep generative modeling, including autoencoders, VAEs, GANs, and diffusion models. Learn about latent variables, training techniques, and recent advances in generative AI.

• YouTube
• 1 hour
• Self-Paced
• Free Video

Alexander Amini

Deep Generative Modeling - MIT 6.S191 Lecture 4

Explore deep generative modeling, including autoencoders, VAEs, GANs, and recent advances. Learn about latent variables, training techniques, and applications in AI-driven content creation and data analysis.

• YouTube
• 56 minutes
• Self-Paced
• Free Video

AnalystPrep

Overview of Equity Portfolio Management - CFA Level III Reading 14 (2025)

Master essential equity portfolio management concepts, from investment universe segmentation to shareholder engagement strategies, preparing for CFA® Level III with comprehensive coverage of income sources, costs, and investment rationales.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Data Science Courses

Variational Autoencoders and Performers in Deep Learning - Lecture 15

Delve into advanced deep learning concepts including Variational Autoencoders (VAE), variational inference, ELBO computation, and Performer-based Transformer architectures.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

Yannic Kilcher

The Free Transformer and Variational Autoencoder Analysis

Dive into a detailed analysis of the Free Transformer paper, exploring how decoder Transformers can be enhanced with unsupervised latent variables using variational methods.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

Generative Models - Representation Learning Meets Generative Modeling - Lecture 15

Explore the intersection of representation learning and generative modeling through VAEs and latent variables in this MIT deep learning lecture.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video