Gabriel Melo

Teaching

As part of my academic and research interests, I am currently involved as a teaching assistant in the following courses:

• Numerical Analysis - Monte Carlo Methods (Graduate Level)

  Télécom Paris M1 Engineering Cycle • Institut Polytechnique de Paris

  Inverse Transform Sampling, Monte Carlo, Rejection Sampling, Importance Sampling, Markov Chain Monte Carlo (MCMC)

• Optimization for Machine Learning (Graduate Level)

  Télécom Paris M1 Engineering Cycle • Institut Polytechnique de Paris

  Convex Analysis and Optimization (subdifferential, duality, lagrangian, etc), Stochastic gradient methods and Proximal operator methods.

• Advanced Statistics (Graduate Level)

  Télécom Paris M1 Engineering Cycle • Institut Polytechnique de Paris

  Non-parametric Statistics, Density estimator, Nadaraya-Watson, Splines, Intro to RKHS

• Kernel Methods (Graduate Level)

  École Polytechnique M2 DataScience • Institut Polytechnique de Paris

  Reproducing Kernel Hilbert Spaces; Kernel machines for regression, classification and dimensionality reduction; MMD and Integral Probabilities Metrics

• Statistics (Graduate Level)

  Télécom Paris M1 Engineering Cycle • Institut Polytechnique de Paris

  Models and estimators, Cramer-Rao Bound, Fisher Information, hypothesis testing, confidence intervals, bayesian methods

• Machine Learning (Graduate Level)

  Télécom Paris M1 Engineering Cycle • Institut Polytechnique de Paris

  This course covers the foundations of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and neural networks.

• Natural Language Processing (Graduate Level)

  Télécom Paris M1 Engineering Cycle • Institut Polytechnique de Paris

  This course introduces students to the field of NLP, covering text preprocessing, word embeddings, sequence models, and deep learning approaches for text analysis.