Teaching
DDA4210 Advanced Machine Learning
Sylabus
* Introduction and review
* Advanced ensemble learning
* Learning theory
* Advanced applications: recommendation and search
* Spectral clustering and semi-supervised learning
* Graph neural networks
* Nonlinear dimensionality reduction and data visualization
* Generative models (VAE, GAN, diffusion model)
* Causal machine learning
* Privacy in machine learning
* Safety and fairness
* Interpretability and explainability
* Course project presentation and review