Machine Learning

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

Tracking Urban Expansion Through Satellite Imagery

December 12, 2023
Leïla Njee Bugha
by Leïla Njee Bugha. Among its many uses, remote sensing can prove especially useful to document changes and trends from eras or settings, where traditional sources are either inexistent or infrequently collected. This is the case when one wants to study urban expansion in sub-Saharan countries over the past 20 years. To further remedy the lack of data on land cover uses from earlier time periods, classification methods can be used as well. Using easily accessible satellite imagery from Google Earth Engine, I provide here an example combining remote sensing with classification to detect changes in the land cover in Nigeria since 2000 due to urban expansion.

Hugh Kadhem

Data Science Fellow
Mathematics

Hugh Kadhem is a Ph.D. student in Applied Mathematics, with broad research interests in computational quantum physics and high-performance scientific computing.

Artificial Intelligence and the Mental Health Space: Current Failures and Future Directions

October 31, 2023
María Martín López
by María Martín López. María Martín López, a PhD student in the department of psychology whose research focuses on large language models within the context of mental illness, gives an overview of current failures and possible future directions of NLP models in the mental health space. She brings up questions that must be considered by all researchers working in this space and encourages these individuals to think creatively about the use of AI beyond direct treatment.

María Martín López

Data Science Fellow
Psychology

María Martín López is a PhD student in the Cognition area within the Department of Psychology. Her research relates to cognitive computational and quantitative models of individual differences in behaviors, thoughts, and emotions. She is particularly interested in how we can create and leverage novel algorithms to understand, measure, and predict processes relating to externalizing psychopathology (e.g. impulsivity, aggression, substance use). She answers these questions using a range of computational and quantitive models including AI, NLP, SEM, time series analysis, multi-level...

Python Deep Learning: Parts 1-2

November 13, 2023, 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.

Python Machine Learning Fundamentals: Parts 1-2

November 7, 2023, 9:00am
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.

Using Artificial Intelligence to Help Write Code

February 28, 2023
Daniel Tan
by Daniel Tan. ChatGPT is a natural language processing model that has applications in a wide variety of research settings. It is a chatbot-style tool that was created by OpenAI using a deep learning model that allows it to generate human-like responses to a wide variety of questions and prompts spanning a multitude of topics. Because it has been trained on a large body of text, ChatGPT is a particularly useful tool for programming. This post explores ways to use ChatGPT to help write code in Stata, a statistical software package that is widely used in academic and policy research.

Can Machine Learning Models Predict Reality TV Winners? The Case of Survivor

March 14, 2023
Kelly Quinn
by Kelly Quinn. Reality television shows are notorious for tipping the scales to favor certain players they want to see win, but could producers also be spoiling the results in the process? Drawing on data about Survivor, I attempt to predict the likelihood of a contestant making it far into the game based on editing and production decisions, as well as demographic information. This post describes the model used to classify player outcomes and other potential ways to leverage data about reality TV shows for prediction.

Why We Need Digital Hermeneutics

July 13, 2023
Tom van Nuenen
by Tom van Nuenen. Tom van Nuenen discusses the sixth iteration of his course named Digital Hermeneutics at Berkeley. The class teaches the practices of data science and text analysis in the context of hermeneutics, the study of interpretation. In the course, students analyze texts from Reddit communities, focusing on how these communities make sense of the world. This task combines both close and distant readings of texts, as students employ computational tools to find broader patterns and themes. The article reflects on the rise of AI language models like ChatGPT, and how these machines interpret human interpretations. The popularity and profitability of language models presents an issue for the future of open research, due to the monetization of social media data.

My Summer Exploring Data Science for Social Justice: Learnings, Tensions & Recommendations

September 5, 2023
Genevieve Smith
by Genevieve Smith. This summer I joined the D-Lab hosted Data Science for Social Justice workshop at UC Berkeley diving into Python – including TF-IDF, sentiment analysis, word embeddings, and more – with a lens towards leveraging data science for social justice. My team explored a Reddit channel on abortion and used computational analysis to answer key questions related to abortion access from before versus after Roe vs. Wade was overturned. Computational social science is incredibly powerful, but I continue to grapple with tensions particularly as it relates to employing machine learning and large language in international research, and end with key recommendations for CSS practitioners.