Teaching

Optimization

In this course: Problem statement, The curse of dimensionality, Convex functions, Continuous differentiable functions, Gradient descent, Black-box optimization and Stochastic optimization,  Evolutionary algorithms,  Evolution of Distribution Algorithms (EDAs),  Summary.

Requirements: Multivariable calculus.

From Optimization to Machine Learning

In this course: Supervised and unsupervised learning, Generalization, Supervised Example: Linear classification, Clustering and K-means, Polynomial regression, Model selection, Changing representations, Summary.

Machine Learning with probabilities

In this course: Notions in probability theory, Sampling from complex distributions, Density estimation, Maximum a-posteriori and maximum likelihood, Choosing a prior, Maximum likelihood for the Gaussian, Probabilistic polynomial regression, Latent variables and Expectation Maximization, Gaussian mixtures and EM, Optimization revisited in the context of maximum likelihood, Summary.