François Chung, Ph.D.

Tag: computer simulation

Business process and decision modeling

Business process and decision modeling

HPI training, MOOC (2021). This online training introduces concepts of business process modeling using the Business Process Model and Notation (BPMN) industry standard. Based on a thorough understanding of BPMN, the last part of the training covers decision models using the Decision Model and Notation (DMN). Decision models complement process models by representing concrete, operational decisions, both with their structure and their decision logics.

Week 1: Introduction to business process management

Main topics:

  • Defining business processes;
  • Business process models;
  • Interacting business processes;
  • Models and instances;
  • Business process lifecycle.

Week 2: Basic business process modeling

Main topics:

  • Process activities;
  • Exclusive and parallel gateways;
  • Inclusive gateways and loops;
  • Start, intermediate and end events;
  • Concurrency.

Week 3: Analyzing the behavior of process models

Main topics:

  • Process behavior;
  • Structural soundness;
  • Simulating business processes;
  • Petri nets and process analysis;
  • Checking soundness.

Week 4: Advanced business process modeling

Main topics:

  • Sub-processes and boundary events;
  • Activity modifiers;
  • Event-based gateway;
  • Modeling organizations;
  • Resource allocation patterns.

Week 5: Data in business process models

Main topics:

  • Organizing process models;
  • Data and data flow;
  • Data execution semantics;
  • Structured data and sub-processes;
  • Object lifecycle conformance.

Week 6: Business decision modeling

Main topics:

  • Implementation of decisions;
  • Decision requirements diagrams;
  • Semantics of decision tables;
  • Analysis of decision tables;
  • Consistency of processes and decisions.

References

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BPMN - Business Process Model and Notation
DMN - Decision Model and Notation
openHPI - Hasso Plattner Institute

Quantum computing and physics

Quantum computing and physics

Udemy training, MOOC (2020). This online training presents quantum computing as the next wave of the software industry. Quantum computers are exponentially faster than classical computers of today. Problems that were considered too difficult for computers to solve, such as simulation of protein folding in biological systems and cracking RSA encryption, are now possible through quantum computers. The training is primarily about analyzing the behavior of quantum circuits using math and quantum physics.

Section 1: Introduction

Main topics:

  • Why learn about quantum computing?
  • How is quantum computing different?

Section 2: Quantum cryptography

Main topics:

  • Experiments with photon polarization;
  • No-cloning theorem;
  • Encoding with XOR;
  • Encryption with single-use shared-secrets;
  • Encoding data in photon polarization.

Section 3: Foundation

Main topics:

  • Probability;
  • Complex numbers;
  • Matrix algebra;
  • Matrix multiplication;
  • Logic circuits.

Section 4: Math model for quantum physics

Main topics:

  • Modeling physics with math;
  • Substractive probabilities through complex numbers;
  • Modeling superposition through matrices.

Section 5: Quantum physics of spin states

Main topics:

  • Matrix representation of quantum state;
  • State vector;
  • Experiments with spin.

Section 6: Modeling quantum spin states with math

Main topics:

  • Analysis of experiments;
  • Dirac bra-ket notation;
  • Random behavior.

Section 7: Reversible and irreversible state transformations

Main topics:

  • Irreversible transformations measurement;
  • Reversible state transformations.

Section 8: Multi-qubit systems

Main topic:

  • Multi-qubit systems.

Section 9: Quantum entanglement

Main topic:

  • Quantum entanglement.

Section 10: Quantum computing model

Main topics:

  • Quantum circuits;
  • Reversible gates;
  • CNOT and CCNOT gates;
  • Universal and Fredkin gates;
  • Superposition and entanglement on quantum gates.

Section 11: Quantum programming with Microsoft Q#

Main topics:

  • Q# simulator hardware architecture;
  • Measuring superposition states;
  • Effect of superposition on quantum gates;
  • Toffoli gate;
  • Programming quantum computers.

Section 12: IBM quantum experience

Main topic:

  • IBM quantum experience.

Section 13: Conclusion

Main topic:

  • Speedup revisited.

References

Training

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Data science specialization

Data science specialization

Coursera training, MOOC (2020). This specialization covers the concepts and tools needed throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. Topics covered include using R to clean, analyze, and visualize data, navigating the entire data science pipeline from data acquisition to publication, using GitHub to manage data science projects, and performing regression analysis, least squares and inference using regression models.

Course 1: Data scientist’s toolbox

Main topics:

  • Data science fundamentals;
  • R and Rstudio;
  • Version control and GitHub;
  • R Markdown, scientific thinking and big data.

Course 2: R programming

Main topics:

  • Background and getting started;
  • Programming with R;
  • Loop functions and debugging;
  • Simulation and code profiling.

Course 3: Getting and cleaning data

Main topics:

  • Finding data and reading different file types;
  • Data storage systems;
  • Organizing, merging and managing data;
  • Text and data manipulation in R.

Course 4: Exploratory data analysis

Main topics:

  • Analytic graphics and base plotting in R;
  • Lattice and ggplot2;
  • Data dimension reduction;
  • Cluster analysis techniques.

Course 5: Reproducible research

Main topics:

  • Concepts, ideas and structure;
  • Markdown and knitr;
  • Reproducible research checklist;
  • Evidence-based data analysis.

Course 6: Statistical inference

Main topics:

  • Probability and expected values;
  • Variability, distribution and asymptote;
  • Intervals, testing and p-value;
  • Power, bootstrapping and permutation tests.

Course 7: Regression models

Main topics:

  • Least squares and linear regression;
  • Linear and multivariate regression;
  • Residuals and diagnostics;
  • Logistic and Poisson regression.

Course 8: Practical machine learning

Main topics:

  • Prediction, errors and cross validation;
  • Caret package;
  • Decision trees and random forests;
  • Regularized regression and combining predictors.

Course 9: Developing data products

Main topics:

  • Shiny, GoogleVis and Plotly;
  • R Markdown and Leaflet;
  • R Pakages and Swirl.

References

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DataNews 2020 – Magazine article

DataNews 2020 (FR) – Magazine article

Publication

François Chung; Combler le fossé quantique, aujourd’hui; DataNews, 2, p. 5, 2020.

Abstract

L’informatique quantique permettra de solutionner des problèmes complexes, impossibles à résoudre avec les ordinateurs d’aujourd’hui. Le Digital Annealer de Fujitsu offre une alternative à l’informatique quantique encore trop coûteuse et difficile à exécuter.

References

Publication

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DataNews 2020 – Magazine article

DataNews 2020 (NL) – Magazine article

Publication

François Chung; De kwantumkloof dichten, vandaag de dag; DataNews, 2, p. 5, 2020.

Abstract

Kwantuminformatica biedt een oplossing voor complexe problemen, die niet kunnen opgelost worden met de huidige computersystemen. De Digital Annealer van Fujitsu biedt een alternatief voor de kwantuminformatica, die momenteel nog te duur en te moeilijk uit te voeren is.

References

Publication

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