MLVU 12.4: Graph models @riskone1

MLVU 12.4: Graph models @riskone1

Uploaded March 2021 | Updated September 2026, 5 days ago

We take a high level look at some ways to do machine learning on graph data.

slides: mlvu.github.io/lectures/62.Matrices.annotated.pdf
lecturer: Peter Bloem

MLVU 12.4: Graph models

13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo, AlphaStar (MLVU2019)

2 Linear Models 1: Hyperplanes, Random Search, Gradient Descent (MLVU2019)

MLVU 7.4: Making it work

10 Tree Models and Ensembles: Decision Trees, AdaBoost, Gradient Boosting (MLVU2020)

01 Introduction to Machine Learning (MLVU2018)

MLVU 7.3: Convolutions

9 Deep Learning 2: Generative models, GANs, Variational Autoencoders (VAEs) (MLVU2019)

MLVU 10.3: Ensembling: stacking, bagging and random forests

MLVU 5.1: Introduction to probability

09 Deep Learning 2: GANs, Variational Autoencoders (MLVU2018)

4 Methodology for pre-processing, PCA, Eigenfaces (MLVU2020)

MLVU 12.4: Graph models