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Local information technology people sharing passion and learning.
January: -- no meeting --
January: -- no meeting --
January 5, 2022 · 6:00 PM at no meeting
February: -- no meeting --
February: -- no meeting --
February 2, 2022 · 6:00 PM at no meeting
March: -- no meeting --
March: -- no meeting --
March 2, 2022 · 6:00 PM at no meeting
April: -- no meeting --
April: -- no meeting --
April 6, 2022 · 6:15 PM at no meeting
May: Loop or Model-Based Learning for Continuous Improvement with Greg Cupal
May: Loop or Model-Based Learning for Continuous Improvement with Greg Cupal
May 4, 2022 · 6:00 PM at on-line meeting
This talk will be given at Agile & Beyond 2022 on Tue May 24 - Wed 25.
The event on Ann Arbor Tech Meetup.
Sponsored by Arbormoon Software, Inc.:
Several problem-solving and learning techniques from multiple industries share many techniques and behaviors. This session will investigate those from a Scientific Method and Systems Thinking perspective.
The problem-solving techniques examined may include:
Scientific Method,
Systems Thinking,
Single-Double-Triple-loop learning,
Validated Learning (Lean Startup),
OODA Loop,
PDC(S)A Cycle,
SCRUM,
and others.
Transform your plan into a hypothesis. Test your plan during execution. Study the results of your actions in a retrospective. Compare the reality of the execution to the "mental model" of your plan, learn, repeat.
The class will conclude with a discussion of Emergent Behavior in Systems Thinking. Emergent Behavior is a gateway into Complexity Theory and VUCA topics.
About the Presenter
Greg became an advocate for Systems and Model-based learning early in his career. Inspired by Deming's "Appreciation of a System" and LEAN thinking, he has applied these concepts to workflow, problem-solving, and incremental (loop) learning. This practice introduced him to Systems Thinking, Complexity Theory, and other VUCA topics.
June: A Whirlwind Tour of Unsupervised Machine Learning Methods with Cameron Fen
June: A Whirlwind Tour of Unsupervised Machine Learning Methods with Cameron Fen
The event on Ann Arbor Tech Meetup.
Wednesday, June 1, 2022 · 6:00 PM at Online
Sponsored by Arbormoon Software, Inc.:
Machine learning refers to the science of building mathematical models to predict elements of interest. The objective of this talk is to introduce these machine learning models to software engineers. I will focus on the lesser known, but more important problem of unsupervised learning. Unsupervised learning refers to learning interesting statistics using only x data without any labels y telling the model what the correct prediction at training time is. For example, the internet mostly contains unsupervised data suggesting that if we want to build artificial intelligence that can utilize the whole gamut of human experience, we need to have powerful unsupervised models. This presentation will assume only a basic understanding of machine learning up to a basic understanding of linear regression. However, I will still cover the cutting-edge models in machine learning including GANs, Normalizing Flows, Contrastive Models, and Transformers.
About the Presenter:
Cameron Fen is a data scientist, quantitative trader, and PhD student in economics. As a graduate student, he specializes in using the tools of deep learning to improve macroeconomic models. He has written papers on recurrent neural network economic forecasting, improvements in external validity using pooled data, and simulation-based inference for Bayesian estimation of dynamic macroeconomic models. He has presented at the Econometric Society, EcoMod, and Allied Social Science Association. Additionally, he was cofounder and head of research at AICM a quantitative trading startup which received support and funding from Mass Challenge, Nvidia, and the NexCubed Incubator. He has also been interviewed by Information Week, Search Enterprise AI, and Authority Magazine. You can find more about his research at cameronfen.github.io.
July: Command Query Responsibility Segregation (CQRS) in C# with Phil
July: Command Query Responsibility Segregation (CQRS) in C# with Phil
The event on Ann Arbor Tech Meetup.
Wednesday, July 6, 2022 · 6:00 PM at Online
Sponsored by Arbormoon Software, Inc.:
Much of programming is interactions of storing and retrieving records (CRUD operations). Command Query Responsibility Segregation (CQRS) is about separating model responsibilities for storing and retrieving records. CQRS is not about code reuses, but different models for each function. Phil will compare examples of a conventional data layer and using CQRS.
Agenda
Clean Architecture (theory)
Command Query Responsibility Segregation (theory)
Code Generation
MediatR
Data Layer Code Example
CQRS Query Code Example
CQRS Command Code Example
About the Presenter:
Phil is a programmer. He has been a programmer analyst, data communication programmer, systems engineer, DBA, Windows and UNIX system admin and consultant/mentor. He is currently coding for himself.
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