Professor - Minimum course for the students of the Master in information technologies and data management. This course gives the students a deep understanding of the Data Mining and Data Warehousing principles. It starts presenting strategies for building warehouse systems; then the lessons go through applications for visualizing and querying data by non-experts. The primary objective is to drive management decisions and marketing plans using these techniques. Mainly the customer relationship management (CRM).
Adjunct Professor - 2th- and 6th-year university students. In this course, we study (and mainly implement) advanced topics in Computer Programming such as object-oriented design, data structures, functional programming, threading, simulation, metaprogramming, input/output, unit testing and graphical interfaces. This book: "Advanced Computer Programming in Python" summarizes all the contents of this course.
Adjunct Professor - 4th- and 6th-year university students. Analysis and implementation of basic techniques and algorithms in data mining. We study data warehouses, ETL, data preprocessing, data visualization, association rules, linear regression, classification algorithms (logistic regression, decision trees, random forest, KNN), clustering methods (k-means, hierarchical, and gaussian mixtures with EM).
* We have submitted the manuscript to Data Mining and Knowledge Discovery. The paper is in the review process.
Google Scholar profile
It was a flip classroom environment. I was in charge of creating evaluations and weekly practical activities for more than a hundred students.
In addition, I oversaw a 25-member team. Together we gave feedback and graded students' assestments.
Finally, I supported this class' professors on developing and publishing the book "Advanced Computer Programming in Python", which includes the developed material.
I also served as TA for some introductory courses related to computer programming.
2017 - TA
We studied machine learning from a probabilistic perspective.
Bayesian learning: the beta-binomial and the Dirichlet-multinomial model.
Monte Carlo Inference: I also learned and developed customized implementations of rejection sampling, importance sampling.
MCMC inference: we solved problems implementing Gibbs sampling, metropolis hastings, annealing methods.
Variational inference: KL divergence, the mean field method, and Variational Bayes EM.
2015 - 2016 - TA
This course teaches the fundamental data structures and their main algorithms. We evaluated complexity on memory and time. We also studied the main techniques for solving discrete optimizations.
We implemented and evaluated: linked lists, queues, heaps, hash tables, trees (binary, red-black, B), graphs (BFS, DFS), dictionaries, prioritized queues, disjoint sets, greedy algorithms (Dijkstra, minimum cost coverage), divide to conquer, dynamic programming, and sorting algorithms.
2017 - TA
This course teaches widely used tools and applications for automatic analysis and data mining processes. Starting by presenting strategies for building warehouse systems, the lessons go through applications for visualizing and querying data by non-experts.
This subject gave me a big picture of every piece of software and model I developed during my time as a data scientist. Now I know how they fit together.
2015 - 2016 - Chief TA
The course introduces the basis of stochastic modeling systems. This course presents basic techniques and concepts under the most widely used analytics models in operations research for representing probabilistic systems.
We studied Poisson processes, discrete-time Markov chains, and continuous-time Markov chains. We also learned process simulation.
2016 - Chief TA
4th- and 6th-year university students. Analysis and implementation of basic techniques and algorithms in data mining. We study data warehouses, ETL, data preprocessing, data visualization, association rules, linear regression, classification algorithms (logistic regression, decision trees, random forest, KNN), clustering methods (k-means, hierarchical, and gaussian mixtures with EM).
2018 Data Mining course, 100% satisfaction & 100% recommendation level, supported by students. Engineering School of the Pontifical Catholic University of Chile.
2017 Awarded by the Engineering School of the Pontifical Catholic University of Chile.
2016-2017 Engineering School of the Pontifical Catholic University of Chile gave me a scholarship for pursuing my entire master research.
2015 Acknowledgment for Great Quality of Support to Teaching (Teacher Assistant), 2015. Courses: Advanced Programming and Stochastic Models. Pontifical Catholic University of Chile.
2012 Honor enrollment - Pontifical Catholic University of Chile.
2012 National Math top score - University Admission 2012. Ministry of Education of Chile