Representation Learning Courses and Certifications

Simons Institute

Continuous State Machines and Grammars for Linguistic Structure Prediction

Explore linguistic structure prediction using continuous state machines and grammars. Learn about dependency parsing, RNNs, and advanced techniques for multilingual and semantic parsing.

• YouTube
• 38 minutes
• On-Demand
• Free Video

Valence Labs

Additive Decoders for Latent Variables Identification

Explore additive decoders for latent variable identification and out-of-support image generation in representation learning. Gain insights into nonlinear ICA and object-centric representation learning.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Valence Labs

Learning Representations on Biological Data with Weakly Supervised Learning

Explore weakly supervised learning for biological data representations. Discover PeML method for improved similarity metrics in perturbational experiments, enhancing mechanism of action identification and downstream analysis tasks.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

Valence Labs

Unsupervised Learning of Group Invariant and Equivariant Representations

Explore unsupervised learning of group invariant and equivariant representations. Gain insights into symmetry in data, practical examples, and results for graphs and various symmetries.

• YouTube
• 1 hour 32 minutes
• On-Demand
• Free Video

MITCBMM

The Platonic Representation Hypothesis

Explore the Platonic Representation Hypothesis with MIT's Phillip Isola, delving into philosophical concepts and their applications in modern computational theory.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Learning Representations of High-Dimensional Stochastic Systems

Discover how to learn representations of complex high-dimensional stochastic systems through advanced mathematical and computational approaches in biological contexts.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Latent-Variable Representations for Speech Processing and Research

Explore latent-variable representations and their applications in speech processing research through advanced computational methods and practical implementations.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Aleksa Gordić - The AI Epiphany

Graph SAGE - Inductive Representation Learning on Large Graphs - GNN Paper Explained

Deep dive into Graph SAGE, exploring its innovative approach to large-scale graph learning. Covers key concepts, training methods, aggregator functions, and comparisons with other graph neural networks.

• YouTube
• 44 minutes
• On-Demand
• Free Video

DataLearning@ICL

Causal Representation Learning - Data Learning Seminar

Explore causal representation learning with Johann Brehmer, delving into cutting-edge techniques for understanding cause-effect relationships in data science and machine learning.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video

UC Berkeley EECS

Causal Representation Learning and Optimal Intervention Design

Explore advanced concepts in causal representation learning and intervention design, focusing on mathematical frameworks and practical applications in modern machine learning systems.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

The Information Knot - Tying Sensing and Action; Emergence Theory of Representation Learning

Explore information theory, representation learning, and AI concepts with NYU's Stefano Soatto. Delve into information bottlenecks, mutual information, and disentangling in this advanced seminar on modern AI.

• YouTube
• 55 minutes
• On-Demand
• 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
• On-Demand
• Free Video

Representation Learning - Reconstruction-Based - Lecture 11

Explore reconstruction-based representation learning through autoencoders, clustering, vector quantization, and self-supervised methods with neural network insights.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Representation Learning: Similarity-Based - Lecture 12

Explore similarity-based representation learning through metric learning, contrastive methods, InfoNCE loss, and alignment principles for effective deep learning applications.

• YouTube
• 1 hour 16 minutes
• On-Demand
• Free Video

Representation Learning - Theory - Lecture 13

Explore the theoretical foundations of representation learning, examining architectural inductive biases and neural network connections to Gaussian processes.

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video