Presented by Charles Parker

A basic problem in computer science is binary classification, in which an algorithm applies a binary label to data based on the presence or absence of some phenomenon. Problems of this type abound in areas as diverse as computational biology, multimedia indexing, and anomaly detection. Evaluating the performance of a binary labeling algorithm is itself a complex task, often based on a domain-dependent notion of the relative cost of "false positives" versus "false negatives". As these costs are often not available to researchers or engineers, a number of methods are used to provide a cost-independent analysis of performance. In this talk, I will examine a number of these methods both theoretically and experimentally. The presented results suggest a set of best practices for evaluating binary classification algorithms, while questioning whether a cost-independent analysis is even possible.

Presented by Erlend Hamberg.

An important problem in genetics is phylogenetic inference: Coming up with good hypotheses for the evolutionary relationship between species – usually represented as a “family tree”. As the amount of molecular data (e.g. DNA sequences) quickly grows, efficient algorithms become increasingly important to analyze this data. A maximum-likelihood approach with models for nucleotide evolution allows us to use all the sequence data, but is a computationally expensive approach. The number of possible trees also grows rapidly as we include more species. It is therefore necessary to use heuristic search methods to find good hypotheses for the “true” tree. Evolutionary algorithms (EA) is a class of such search/optimization algorithms that has been shown to perform well in other areas where the search space is large and irregular. I will explain my approach and my findings from using an evolutionary algorithm for inferring phylogenies from molecular data.

Join us for Algorithms as Recipes for Computer Programming, presented by Rebekah Golden, a local developer at JanRain.

Algorithms sometimes intimidate but really they are just recipes for computers to follow to do repetitive tasks. This is a beginner class on recognizing when an algorithm would be useful, basic methods for creating one in Python, and a quick look at algorithms that have been written by others for common tasks. Presentation is in a room without computers but if you bring yours you can build a computer recipe or two yourself. Discussion and repetitive task descriptions welcome.

This meeting is open to the public. People of all genders are welcome to attend.

Check back for a rescheduled time.

Sunil Rao will be presenting his past research on SNOTEL data using the time series prediction.

Drought is a serious problem in much of the U.S., with the worst conditions across the southern and western parts of the nation. Much of irrigation and recreation facilities depend on proper forecasting of streamflow. The water supply for irrigation largely comes from rivers and creeks, whose streamflow originates from the springtime melting of winter snow. A water supply forecast is a prediction of streamflow volume that will flow past a point on a stream during a specified season, typically in the spring and summer. One of the primary sources for the data is through NRCS SNOTEL( Snow Telemetry) data (available to public as part of tax dollars at work). In this demo, we showcase one such tool (Timeseries ARIMAX model) to forecast Streamflow volume for Deschutes River Basin, OR and later compare with actual data to see how it performed.

If you would like to join the discussion check us out on Zoom https://zoom.us/j/7891236789.

Do you want to learn and share your passion in a supportive community? Knowledge Mavens is an ethos of sharing, creativity, and inspiration.

Our Meetup provides an opportunity to "Show and Tell" followed by feedback and Q&A. You'll have the opportunity to share with our channels such as Meetup, GitHub, YouTube, and Facebook to connect with more passionate people.

The second half of our session we'll collaborate on new topics. The winner wins an award for the most interesting topic and the opportunity to share in an upcoming session.

In this Meetup, we're going to have fun coding together, so bring your laptop if you can. Our plan is to run through coding examples from https://www.freecodecamp.org/. I'll give you a problem, you can try to solve it, then we'll work on it together. We'll be going through the JavaScript algorithms and data structures section.

You're welcome to join us and code other projects as well.

Be inspired! Knowledge Mavens

In this presentation, we'll learn about the mathematical basis of big-o notation and how it's used to characterize the complexity of various algorithms. The presentation is tailored toward for those who already understand the basics of coding (lists, loops, etc) and are preparing for technical interviews. Presented by PDX Code Guild Instructor Matthew Cooper

Chris Thompson, Machine Learning Engineer, will be joining us to discuss Algorithms and Data Structures.

Instead of being seen as concepts or tools that can make a developer's life easier, new and seasoned programmers often fear and ignore data structures and algorithms. To build high-quality, scalable software and web applications or get a coveted job at a FAANG company, you must know and master these topics.

In this talk, Chris will cover:

-What data structures and algorithms are -Why you should learn them and -Materials he has found helpful in learning these topics

Join us Friday, November 18th at 1 pm Pacific Time (2p MT, 3p CT, 4p ET) in our Google Meet room.

RSVP on Meetup: https://bit.ly/TechTalk1118 RSVP on Eventbrite: https://bit.ly/TechTalkE1118

Can't make it live? We will be posting the recording to our Tech Talks playlist on our YouTube Channel: https://bit.ly/TTA-techtalks

Don't miss this great opportunity to learn and network!

Last month, we heard about the use of Artificial Intelligence (AI) technologies in our educational system, but these technologies are also being incorporated into many other commercial and social enterprises that impact our daily lives, including the fields of medicine, journalism, finance, human resources, law enforcement, and transportation, just to name a few.

While AI technologies may be beneficial to society, how do we know that the systems being developed are trustworthy and that they actually do what their creators claim? Can developers explain how their AI systems work and demonstrate that the outputs they generate are not biased? How might governments regulate these systems? Should companies be allowed to regulate themselves? How might governments and companies work together to ensure fairness and understandability of what the systems are doing?

Last year, World Privacy Forum, a privacy-focused research nonprofit, studied various AI governance tools currently in use around the world. They recently published their findings via a report that was co-authored by Pam Dixon, executive director of World Privacy Forum, and Kate Kaye, deputy director of the organization: https://www.worldprivacyforum.org/2023/12/new-report-risky-analysis-assessing-and-improving-ai-governance-tools/

At this month's meeting, World Privacy Forum’s Kate Kaye will join us to share the details of their research methodologies and what they learned about how governments are overseeing the implementation of AI in their countries. She'll give an overview of what AI is and what it does, and she'll also present some examples of both effective and ineffective approaches to good governance of these systems.

Bring your questions and thoughts about AI governance, and come join the discussion!

Please RSVP via this Meetup page or by sending an email to [email protected].

Speaker bio:

Kate Kaye is a Portland resident and deputy director of World Privacy Forum, a nonpartisan public-interest research nonprofit. Her research focuses on the implications of AI, digital identity and health data ecosystems, data governance, and other issues related to data collection, use and privacy.

Before joining World Privacy Forum, Kate worked for more than 20 years as an award-winning journalist covering data, emerging technology and the impact of tech on people and society. Her reporting has been seen and heard in MIT Technology Review, NPR, Protocol, Bloomberg CityLab, OneZero, WSJ, Fast Company, and other media outlets.

Kate is the founder of tech and AI ethics reporting website RedTailMedia.org. RedTail has been home to some of her work investigating algorithmic and surveillance tech policy and use in Portland including Banned in PDX, a podcast series about Portland’s facial recognition ban, and an investigation of the city’s collapsed partnership with Google-sibling Replica, a location and mobility tracking company. Kate is the author of the 2009 book on digital voter data use, Campaign ’08: A turning point for digital media.

By attending this TA3M meeting, you agree to follow our Code of Conduct: https://www.meetup.com/Portlands-Techno-Activism-3rd-Mondays/pages/22681732/Code_of_Conduct/

{short} Code of Conduct Portland's Techno-Activism 3rd Mondays is dedicated to providing an informative and positive experience for everyone who participates in or supports our community, regardless of gender, gender identity and expression, sexual orientation, ability, physical appearance, body size, race, ethnicity, age, religion, socioeconomic status, caste, or creed.

Our events are intended to educate and share information related to technology and activism, and anyone who is there for this purpose is welcome. Because we value the safety and security of our members and strive to have an inclusive community, we do not tolerate harassment of members or event participants in any form.

Audio and video recording are not permitted at meetings without prior approval.

Our Code of Conduct (https://www.meetup.com/Portlands-Techno-Activism-3rd-Mondays/pages/22681732/Code_of_Conduct/) applies to all events run by Portland's TA3M. Please report any incidents to the event organizer.