Group Dynamics Seminar Series
The Group Dynamics Seminar series is considered one of the longest running seminar series in the social sciences. It has been running uninterruptedly since it was founded by Kurt Lewin in the 1920’s in Berlin. A very important feature of this seminar today is its interdisciplinary nature. Recent seminars have included discussions in “Law and Psychology,” “Racism and Discrimination,” “Social Media,” and “New Directions in Social Psychology”
ISR Series on Machine Learning
Starting January 25, 2021, Mondays 3:30-5pm, virtual via Zoom*
Machine learning is becoming an increasingly popular tool in many scientific disciplines, including but not limited to data science, medicine, engineering, and business analytics. This event series at the Institute for Social Research (ISR) during Winter 2021 explores the opportunities and available resources for adopting machine learning methods in the social sciences. It is geared at ISR faculty, research staff and students. The series is jointly organized by Jule Krüger, ISR Program Manager for Big Data and Data Science, Meghan Richey, ARC-TS Machine Learning Specialist Senior, and Richard Gonzalez, Director of ISR’s Research Center for Group Dynamics.
[Jan 25] ISR Data Science Program overview, Machine Learning series outline
This opening session will present the data science program at the Institute for Social Research, highlighting available resources and engagement opportunities. It will also provide an overview of this machine learning series. Attendees will have an opportunity to ask questions and provide input.
[Feb 1] A basic introduction: What is Machine Learning (ML)?
Machine learning is becoming an increasingly popular tool in several fields, including data science, medicine, engineering, and business. This workshop will cover basic concepts related to machine learning, including definitions of basic terms, sample applications, and methods for deciding whether your project is a good fit for machine learning. No prior knowledge or coding experience is required.
[Feb 8] Research panel: Machine Learning application examples at the ISR
This research panel will feature presentations from several social science research projects at the ISR that already apply machine learning today. Presenters will share their research logic, implementation strategies, as well as what challenges they might encounter and how they address them. The session will conclude with a discussion and Q&A.
[Feb 15] Project report: Using ML with support from ARC-TS Consultation Services
Come listen to a discussion between Meghan Richey, Machine Learning Specialist Senior with the ARC-TS Consultation Service, and a research project team from the School of Environment and Sustainability. We will discuss an overview of the project, how machine learning was used in a computer vision application, and how the team collaborated to meet milestones.
[Feb 22] Ask an expert office hour: Can I apply ML in my own research project?
In this virtual office hour, machine learning experts at the ISR will be available to consult with interested faculty, research staff and students about possible applications of machine learning methods in current or future research projects. Consultations will be scheduled and facilitated via Zoom breakout rooms. Interested individuals can request a consultation until February 18, 2021, at this link.
[Mar 1] Getting started: Choosing ML tools and training
With many languages, packages, and applications available to researchers, many wonder how to choose the correct path forward when starting a machine learning project. In this workshop, we will discuss the differences between several machine learning packages and applications that are best suited for each available language. A review of basic machine learning concepts, as well as a basic grasp of Python, is recommended.
[Mar 8] Advanced ML topics: Algorithms, writing ML code, comparing implementations
This workshop is designed as a follow-up to the basic introduction to machine learning earlier in this series. We will cover several examples in Python and compare different implementations. We will also look at advanced topics in machine learning, such as GPU optimization, parallel processing, and deep learning. A basic understanding of Python is required.
[Mar 15] Getting funded: Grant writing resources for social science ML applications
In this session, we will cover the U-M campus resources that are available for brainstorming ML projects and writing grant applications. We will share tips for piloting social science research projects that use machine learning, as well as strategies for finding research collaborators in other scientific disciplines.
Additional talks are being planned! Please stay tuned for more dates and detail.
*Anyone authenticated with *umich.edu will be able to join the meeting.