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.

Python Deep Learning: Parts 1-2

June 12, 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 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 Introduction to Machine Learning: Parts 1-2

February 23, 2022, 10:00am
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.

Python Machine Learning Fundamentals: Parts 1-2

February 7, 2023, 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.

Public Talk: Teaching Bias through Word Embeddings

May 16, 2022, 9:00am
This talk by guest speaker, Tom van Nuenen, discusses findings from the Discovering and Attesting Digital Discrimination project which focuses on biases in Machine Learning which proposed a data-driven approach to discover language biases encoded in the vocabulary of discourse communities on social media.

Python Machine Learning Fundamentals: Parts 1-2

October 2, 2023, 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.

R Introduction to Deep Learning: Parts 1-2

November 17, 2021, 10:00am
This workshop introduces the basic concepts of Deep Learning — the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels.

CANCELED: Python Machine Learning Fundamentals: Parts 1-2

November 14, 2022, 4:00pm
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.

Skyler Yumeng Chen

Data Science for Social Justice Fellow 2024
Haas School of Business

Skyler is a Ph.D. student in Behavioral Marketing at the Haas School of Business. Her research centers on consumer behavior and judgment and decision-making, with a keen interest in both experimental methods and data science techniques. She holds a B.A. in Economics and a B.S. in Data Science from New York University Shanghai.

Grace Hu

Data Science for Social Justice Fellow 2024
Bioengineering

Grace is a 3rd year Bioengineering PhD candidate in the joint UC Berkeley-UCSF Graduate Program. Her research lies at the nexus of computational design and 3D-bioprinting to advance tissue engineering for regenerative medicine. She previously studied Materials Science and Engineering (B.S.) and Computer Science (M.S.) at Stanford University, where she investigated printable batteries to power an ultra-affordable scanning electron microscope and explored computer science education research by developing AI models to augment teaching ability.

In her free time she...