Teaching @ Potifical Catholic University of Chile

2018/1 - Data Mining & Management

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).

2018/1 - Advanced Computer Programming

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.

2017/2 - Data Mining

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).

Publications

A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification

Authors: 

  • Belén Saldías F., Computer Science Department,  Pontifical Catholic University of Chile
  • Pavlos Protopapas, Institute for Applied Computational Science, Harvard University
  • Karim Pichara, Computer Science Department,  Pontifical Catholic University of Chile

* We have submitted the manuscript to Data Mining and Knowledge Discovery. The paper is in the review process.
Google Scholar profile

TAs - Teacher Assistances

Advanced Programming

  • Teacher’s Chief Assistant (2013-2015)
  • Course Coordinator (2015-2016)
  • C# (2013-2014)
  • Python (2015-2016)

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, the professors published the book "Advanced Computer Programming in Python" using the developed material. I was one of their main supports in this process.

Approximate Bayesian Inference

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.

Data Structures and Algorithms

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.


Business Intelligence Technologies

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.

Stochastic Models

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.

Data Mining

During 2016 I was the TA of the course that I taught in 2017.

Acknowledgements and Awards

Acknowledgment for High Teaching Quality

2018 Data Mining course, 100% satisfaction & 100% recommendation level, supported by students. Engineering School of the Pontifical Catholic University of Chile.

Best Computer Science Thesis Award

2017 Awarded by the Engineering School of the Pontifical Catholic University of Chile.

Master Degree Excellence Scholarship

2016-2017 Engineering School of the Pontifical Catholic University of Chile gave me a scholarship for pursuing my entire master research.

Acknowledgment for Great Quality of Support to Teaching

2015 Acknowledgment for Great Quality of Support to Teaching (Teacher Assistant), 2015. Courses: Advanced Programming and Stochastic Models. Pontifical Catholic University of Chile.

Honor Enrollment - University Admission 2012

2012 Honor enrollment - Pontifical Catholic University of Chile.

National Math top score - University Admission 2012

2012 National Math top score - University Admission 2012. Ministry of Education of Chile