All courses
Click on the title of a course to see its abstract
1
Communication systems
An introductory unit on digital communication systems where we review all the elements in Shannon's theoretical model of communication using video streaming as a practical example. The topics covered include digital signal processing, image processing, compression, and information theory.
Informatics Engineering, Universidad Austral de Chile, 2018 to 2022, Repository
2
Supervised Machine Learning
A course teaching fundamental concepts of supervised machine learning and also practical applications using the scikit-learn and Pytorch Python libraries.
Informatics Engineering, Universidad Austral de Chile, 2018 to 2022, Repository
3
Statistical Tools for Research
A master-level statistics course covering fundamentals and practical applications using Python and R. It covers modeling, parametric and non-parametric testing, latent variable models, clustering, and Bayesian inference.
Master in Informatics, Universidad Austral de Chile, 2018 to 2022, Repository
4
Linear systems
A course on linear systems to process digital signals. It covers statistical signal processing, Fourier methods, filter design and adaptive filters (LMS, RLS).
Informatics Engineering, Universidad Austral de Chile, 2018 to 2021, Repository
5
Scientific Computing with Python
A course on the suite of Python libraries for scientific computing and data science. The course covers: Interactive data exploration with Jupyter, Manipulating data with Pandas, Data visualization with matplotlib, Linear algebra with Numpy, Numerical optimization with Scipy, Statistics with Scipy, Machine Learning with Scikit-Learn and High-performance Python with Cython.
Informatics Engineering, Universidad Austral de Chile, 2018 to 2022, Repository
6
name "Bayesian Learning and Neural Networks"
This course covers probabilistic programming techniques that scale to massive datasets (Variational Inference), starting from the fundamentals and also reviewing existing implementations with emphasis on training deep neural network models that have a Bayesian interpretation. The objective is to present the student with the state of the art that lays at the intersection between the fields of Bayesian models and Deep Learning through lectures, paper reviews and practical exercises in Python.
Master in Informatics, Universidad Austral de Chile, 2018 to 2022, Repository
7
Mining and Learning from Data
A master-level course on data science and machine learning methodologies where we solve real problems from data reading and manipulation to modeling and inference. I have contributed by guiding projects related to astronomical and audio data.
Master in Informatics, Universidad Austral de Chile, 2018 to 2033, Repository
8
Monte Carlo simulation and MCMC
A course on simulations using the Monte Carlo method and probabilistic generative models. It covers Monte Carlo simulation, Markov Chains and Markov Chain Monte Carlo (MCMC) methods.