Machine Learning

Need help with Machine Learning?

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Below are the consultant we have available with Machine Learning and other expertise listed.

Using Big Data for Development Economics

March 18, 2024
Leïla Njee Bugha
by Leïla Njee Bugha. The proliferation of new sources of data emerging from 20th and 21st century technologies such as social media, internet, and mobile phones offers new opportunities for development economics research. Where such research was limited or impeded by existing data gaps or limited statistical capacity, big data can be used as a stopgap and help accurately quantify economic activity and inform policymaking in many different fields of research. Reduced cost and improved reliability are some key benefits of using big data for development economics, but as with all research designs, it requires thoughtful consideration of potential risks and harms.

Python Machine Learning Fundamentals: Parts 1-2

April 16, 2024, 2:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.

Python Deep Learning: Parts 1-2

March 12, 2024, 2:00pm
The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results.

Dive into the Future of AI with the LLM Working Group at D-Lab

February 7, 2024
Tom van Nuenen, Ph.D.
by Tom van Nuenen, Celebrating 10 years of innovation in data-intensive social science, D-Lab in collaboration with Grad Div is excited to introduce the LLM Working Group, an initiative focused on the exploration and discussion of Large Language Models (LLMs) like ChatGPT within academic research and teaching. This group aims to unite scholars, students, and data scientists to address crucial questions about AI's role in academia, including access, impact, creativity, and learning in the age of information automation. Through a series of interactive sessions, participants will gain insights into LLM capabilities, discuss ethical considerations, and explore innovative approaches to utilizing these tools in their work. Whether you're an AI veteran or a novice curious about the potentials of GenAI, the LLM Working Group offers a collaborative platform to learn, share, and shape the future of academic inquiry. Join us in navigating the world of LLMs together.

Chirag Manghani

Consulting Drop-In Hours: Wed 1pm-3pm

Consulting Areas: Python, R, SQL, Stata, SAS, LaTeX, HTML / CSS, Javascript, C++, APIs, Cloud & HPC Computing, Cybersecurity & Data Security, Databases & SQL, Data Manipulation and Cleaning, Data Science, Data Sources, Data Visualization, Deep Learning, Machine Learning, Natural Language Processing, Python Programming, R Programming, Software Tools, Text Analysis, Web Scraping, Regression Analysis, Software Output Interpretation, Bash or Command Line, Excel, Git or Github, Qualtrics, RStudio, RStudio...

Nicolas Nunez-Sahr

Consulting Drop-In Hours: By appointment only

Consulting Areas: Python, R, SQL, C++, APIs, Databases & SQL, Data Manipulation and Cleaning, Data Science, Data Visualization, Deep Learning, Machine Learning, Natural Language Processing, Python Programming, R Programming, Text Analysis, Regression Analysis, Software Output Interpretation, Bash or Command Line, Git or Github, RStudio, Google Cloud, PostgreSQL, Python Django

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Sanjana Gajendran

Consulting Drop-In Hours: Thu 3pm-5pm

Consulting Areas: Python, SQL, Data Science, Machine Learning, Natural Language Processing, Text Analysis, Git or Github

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R Machine Learning with tidymodels: Parts 1-2

February 27, 2024, 10:00am
Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. During this two part workshop, we will discuss basic features of supervised machine learning algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling using the tidymodels framework. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.

Creating the Ultimate Sweet

January 30, 2024
Emma Turtelboom
by Emma Turtelboom. What is the best Halloween candy? In this blog post, we will identify attributes of popular sweets and create a model to understand how these attributes influence the popularity of the sweet. We’ll discuss alternative model approaches and potential drawbacks, as well as caveats to interpreting the predictions of our model.

Addison Pickrell

IUSE Undergraduate Advisory Board
Mathematics
Sociology

Addison is an aspiring mathematician and social scientist (Class of '27). He loves collecting books he'll never read, is an open-source and open-access advocate, and an aspiring community organizer and systems disrupter. Ask me about community-based participatory action research (CBPAR), critical pedagogy, applied mathematics, and social science.