Javier Orduz

Javier Orduz

Researcher, professor, and Quantum Computing and Machine Learning scientist.

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A list of all the posts and pages found on my site.

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Basic and advance programming

Undergraduate course, Department of Mathematics and Engineering, National Autonomous University of Mexico, 2017

During my time in Mexico I mainly taught programming courses. This is a brief description.

Tiny Machine Learning

Graduate course, Department of Computer Science, Baylor University, 2021

This course shows Tiny Machine Learning principles. This course is in spanish and you can find on EDX-LatinX.

Intro to Computation theory

Graduate course, Department of Computer Science, Baylor University, 2021

This course shows the computing foundations (CS 5310). For each session, I like implementing new pedagogical and technical knowledge to share mathematical and physical concepts. On the BU website, you will figure out more information.

Introduction to Quantum Computing

Graduate course, Department of Computer Science, Baylor University, 2021

This course shows the Quantum Computing foundations (5v93 S1 and S2, 2021 and 2022, respectively). For each session, I like implementing new pedagogical and technical knowledge to share mathematical and physical concepts. On the BU website, you will figure out more information.

Intro to Computation theory

Graduate course, Department of Computer Science, Baylor University, 2022

This course shows the computing foundations (CS 5310). For each session, I like implementing new pedagogical and technical knowledge to share mathematical and computational concepts. On the BU website, you will figure out more information.

Introduction to Quantum Computing

Graduate course, Department of Computer Science, Baylor University, 2022

This course shows the Quantum Computing foundations (5v93 S1 and S2, 2021 and 2022, respectively). For each session, I like implementing new pedagogical and technical knowledge to share mathematical and physical concepts. On the BU website, you will figure out more information.

Physics I

Undergraduate course, Department of Math and Computer Science, Earlham College, 2022

This course (PHYS 120) is an introduction to Physics. It considers a review of basic concepts such as: measurements, vectors, Newton’s laws, and more. In addition, it provides a discussion and review of different topics on Physics.

Statistics Modeling for Data Science

Undergraduate course, Department of Math and Computer Science, Earlham College, 2022

This course (DS 401) is a medium level course. It considers a review of basic concepts such as: Central limit theorem, Confidence intervals, Regressions, models, and more. In addition, it provides implementation with Jupyter Notebooks and applications in different areas.

Math Toolkit

Undergraduate course, Department of Math and Computer Science, Earlham College, 2023

This course (MATH 195) provides students with a review of the basic mathematical tools they need in computer science field. These concepts are every day in computer scientists’ life. We will focus on discrete mathematics; this field will be helpful for a general audience interested in Computer Science. This course contains theory and discussions: it is a course to think, not to calculate. Find material for this course on official website.

Physics II

Undergraduate course, Department of Math and Computer Science, Earlham College, 2023

This course (PHYS 230) is a basic course about Electromagnetism, Waves, and Optics, in this context is an introduction to Physics. It considers a review of basic concepts such as Harmonic motion, Electric charge, Electric Field, Electromagnetic waves, Geometric Optics, and more. We will work on physical and mathematical concepts. We will use an Algebra background; therefore, we will go over some theorems or definitions.

Statistics Modeling for Data Science

Undergraduate course, Department of Math and Computer Science, Earlham College, 2023

This course, advanced DS 401, provides intensive instruction and participation. The meticulously selected latest edition of the course combines a robust set of resources including archives, exercises, interactive activities and engaging lectures, making it a stimulating and engaging learning experience. The course includes a comprehensive review of basic concepts, particularly the central limit theorem, confidence intervals, regression analysis, model building, and several other related topics. In addition, the course seamlessly combines in-depth theoretical understanding with practical skills through comprehensive Jupyter notebook implementations and hands-on applications in a variety of fields.

Advanced Algorithms

Graduate course, Department of Computer Science, Baylor University, 2023

Analysis of algorithms performance, time and space comlexity. Graph algorithms, vector and matrix algorithms, adversary arguments, optimal algorithms, adversart arguments, optimal algorithms, parallel algorithms, and current research topoics. Intense converage of NP-completeness with emphasis on recognizing NP-complete problems, proving NP-completeness and creating approximation algorithms (CS 5350).

Artificial Intelligence and Machine Learning

Undergraduate course, Department of Math and Computer Science, Earlham College, 2024

This course (CS 365) unveils the core principles of intelligence in machines, from its historical roots to cutting-edge applications. It covers their theoretical underpinnings while providing opportunities to put various techniques into practice. Unravel the fundamental concepts of Neural Networks, Convolutional Neural Networks, and the Bayesian version of ML. You build your own AI through interactive labs, tackling real-world challenges. Prepare to shape the future of intelligent systems. This course contains theory and discussions: it is a course to read, learn about history, it contains topics to think, and to calculate. Find material for this course on official website.

Statistics Modeling for Data Science

Undergraduate course, Department of Math and Computer Science, Earlham College, 2024

This course, advanced DS 401, provides intensive instruction and participation. The meticulously selected latest edition of the course combines a robust set of resources including archives, exercises, interactive activities and engaging lectures, making it a stimulating and engaging learning experience. The course includes a comprehensive review of basic concepts, particularly the central limit theorem, confidence intervals, regression analysis, model building, and several other related topics. In addition, the course seamlessly combines in-depth theoretical understanding with practical skills through comprehensive Jupyter notebook implementations and hands-on applications in a variety of fields.

Advanced Algorithms

Graduate course, Department of Computer Science, Baylor University, 2024

Analysis of algorithms performance, time and space comlexity. Graph algorithms, vector and matrix algorithms, adversary arguments, optimal algorithms, adversart arguments, optimal algorithms, parallel algorithms, and current research topoics. Intense converage of NP-completeness with emphasis on recognizing NP-complete problems, proving NP-completeness and creating approximation algorithms (CS 5350).