Advanced Machine Learning

This research-oriented module will focus on advanced machine learning algorithms Probabilistic and Bayesian Machine Learning
Bayesian linear regression
Gaussian Processes (i.e. kernelized Bayesian linear regression)
Approximate Bayesian Inference
Latent Dirichlet Allocation
Beyond the supervised/unsupervised learning problems
Semi-supervised learning
Density-gap based methods
Manifold based methods
Active learning
Selective sampling
Disagreement region coefficient
Sparse models
Advanced Deep learning Techniques
Generative Learning
Denoising auto-encoders
Variational Auto-Encoders
GANs
Learning representations
Word2vec, graph embeddings, …
Randomized algorithms
Random projections, LSH
Primal methods for kernel classifiers (random kitchen sinks)