Machine Learning

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

Python Introduction to Machine Learning: Parts 1-2

May 24, 2022, 1:00pm
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.
See event details for participation information.

What is MLOps? An Introduction to the World of Machine Learning Operations

May 10, 2022
Sam Temlock
More than ever, AI and machine learning (ML) are integral parts of our lives and are tightly coupled with the majority of the products we use on a daily basis. We use AI/ML in almost everything we can think of, from advertising to social media and just going about our daily lives! With the prevalent use of these tools and models, it is essential that, as IT systems and software became a disciplined practice in terms of development, maintainability, and reliability in the early 2000s, ML systems follow a similar trend. The field focused on developing such practices is currently loosely defined under many different titles (e.g., machine learning engineering, applied data science), but is most commonly known as MLOps, or Machine Learning Operations.

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 Introduction to Machine Learning: Parts 1-2

April 25, 2022, 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 28, 2022, 9:00am
This workshop presents a brief history of Artificial Neural Networks (ANNs) and an explanation of the intuition behind them; a step-by-step reconstruction of a very basic ANN, and then how to use the scikit-learn library to implement an ANN for solving a classification problem.

R Introduction to Deep Learning: Parts 1-2

March 29, 2022, 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.

Emily Grabowski

Senior Data Science Fellow, Senior Instructor, Senior Consultant
Linguistics

I am a Ph.D. student in Linguistics. My research interests include understanding how our speech production and speech perception systems constrain linguistic variation, especially as it applies to the larynx. I am also interested in integrating theoretical representations of language with speech. I approach this using a broad variety of tools/methodologies, including theoretical work, experiments, and modeling. Current projects include developing a computational tool to expedite the analysis of pitch and an online perception experiment on the relationship between pitch and perceived...

Spencer Le

Data Peer Consultant, UTech
Computer Science
Data Science

I am a senior majoring in Computer Science and minoring in Data Science. I love crunching down big data and analyzing it in order to help solve real-life issues. In my free time, I like jamming out to music, drawing, studying history, and posting on my foodstagram. If you have any questions regarding Computer Science or Data Science, please stop by!

R Introduction to Machine Learning with tidymodels: Parts 1-2

March 1, 2022, 9:00am
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.

Python Introduction to Machine Learning: Parts 1-2

December 7, 2021, 1:00pm
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.