DDA4210 Advanced Machine Learning
Syllabus
- 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
- Fairness in machine learning
- Interpretability in machine learning
- Course project presentation and review
Slides
PDF
DDA3020 Machine Learning
Syllabus
- Linear regression
- Logistic regression
- Support vector machine
- Decision tree and random forest
- Neural networks I (MLP & CNN)
- Neural networks II (RNN & Transformer)
- Over-fitting, bias-variance trade-off
- Performance evaluation
- Introduction to unsupervised learning
- K-means, Gaussian mixture models
- Expectation Maximization
- PCA
