Machine Learning

Need help with Machine Learning?

Visit Drop-in Hours or Schedule a Consultation: <link to an embedded google calendar OB widget or google form widget> 

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

Python Deep Learning

March 4, 2025, 2:00pm
In this workshop, we will convey the basics of deep learning in Python using keras on image datasets. You will gain a conceptual grasp of deep learning, work with example code that they can modify, and learn about resources for further study.

Finley Golightly

IT Support & Helpdesk Supervisor
Applied Mathematics

Finley joined D-Lab as full-time staff launching their career in Data Science after graduating with a Bachelor's degree in Applied Math from UC Berkeley.

They have been with D-Lab since Fall 2020, formerly as part of the UTech Management team before joining as full-time staff in Fall 2023. They love the learning environment of D-Lab and their favorite part of the job is their co-workers! In their free time, they enjoy reading, boxing, listening to music, and playing Dungeons & Dragons. Feel free to stop by the front desk to ask them any questions or...

Renata Barreto, JD

Research Fellow
Berkeley Law

Renata has a JD and is a Ph.D. candidate at Berkeley, where her research focuses on the harms caused by machine learning models on marginalized groups. She is trained in computational social science and has interned at Twitter and Facebook. She enjoys learning both programming and human languages.

R Machine Learning with tidymodels: Parts 1-2

February 24, 2025, 3:00pm
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.

Python Machine Learning Fundamentals: Parts 1-2

April 8, 2025, 12:00pm
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 Machine Learning Fundamentals: Parts 1-2

February 24, 2025, 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.

Language Models in Mental Health Conversations – How Empathetic Are They Really?

December 3, 2024
Sohail Khan
by Sohail Khan. Language models are becoming integral to daily life as trusted sources of advice. While their utility has expanded from simple tasks like text summarization to more complex interactions, the empathetic quality of their responses is crucial. This article explores methods to assess the emotional appropriateness of these models, using metrics such as BLEU, ROUGE, and Sentence Transformers. By analyzing models like LLaMA in mental health dialogues, we learn that while they suffer through traditional word-based metrics, LLaMA's performance in capturing empathy through semantic similarity is promising. In addition, we must advocate for continuous monitoring to ensure these models support their users' mental well-being effectively.

A Recipe for Reliable Discoveries: Ensuring Stability Throughout Your Data Work

November 19, 2024
Jaewon Saw
by Jaewon Saw. Imagine perfecting a favorite recipe, then sharing it with others, only to find their results differ because of small changes in tools or ingredients. How do you ensure the dish still reflects your original vision? This challenge captures the principle of stability in data science: achieving acceptable consistency in outcomes relative to reasonable perturbations of conditions and methods. In this blog post, I reflect on my research journey and share why grounding data work in stability is essential for reproducibility, adaptability, and trust in the final results.

Python Deep Learning: Parts 1-2

September 24, 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.

Python Deep Learning: Parts 1-2

November 18, 2024, 9:00am
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.