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This workshop will get students started in data analysis using the pandas Python package. It will briefly cover different components of data analysis and connect them with the goal of extracting meaning from data. We will go over an example to illustrate the data analysis process from beginning to end.
Shifting attachments to social groups are a constant in the modern era.They are especially pronounced in the contemporary workplace. What accounts for variation in the strength of organizational identification?
This workshop provides an introduction to effective data visualization in Python. The training focuses on three plotting packages: Matplotlib, Seaborn and Plotly. Examples may include simple static 1D plots, 2D contour maps, heat maps, violin plots, and box plots. The session may also touch on more advanced interactive plots.
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Discrimination is obvious to the people facing discrimination. Given this, do we even need quantitative studies to test if it exists? Regardless of the answer, quantitative studies such as ProPublica’s “Machine Bias” have had a galvanizing effect on racial justice, especially in the context of automated decision-making.
I am an epidemiologist with research interests in developing and applying causal inference methods to understand social and contextual influences on mental health, substance use, and violence in disadvantaged, urban areas of the United States.
In this workshop, participants will learn the basics of various classical machine learning techniques and discuss which types of problems each technique is best suited to address.
Events Archive
Part 1: Introduction to some tools that computer programmers typically use to write and debug code in an effective manner.
Part 2: Introduction to cloud computing (create and manage cloud computing resources) How to use some tools that offer the possibility of writing code locally while seamlessly executing/running it on powerful cloud computing.
Part 1: Introduction to some tools that computer programmers typically use to write and debug code in an effective manner.
Part 2: Introduction to cloud computing (create and manage cloud computing resources) How to use some tools that offer the possibility of writing code locally while seamlessly executing/running it on powerful cloud computing.
Princeton Data Science is hosting coffee chats on Saturday, October 1 and Sunday, October 2. Fill out the form below to be paired with a fellow student interested in data science and get free coffee at Small World Coffee. This is a great opportunity to receive or give mentorship, or simply meet other students who are interested in data science.
Please join us for this intro to Deep Learning workshop. You'll learn about the basics of neural networks with diagrams and code examples in Keras, and work through tutorials to help you get started. You'll need a laptop and internet connection. There's nothing to install in advance, we'll use Colab for all examples. We'll cover the basics …
This 2-day workshop teaches you techniques for training deep neural networks on multi-GPU technology to shorten the training time required for data-intensive applications.
This workshop will get participants started in data analysis using R/RStudio. It will briefly cover different components of data analysis and connect them with the goal of extracting meaning from data. We will go over an example to illustrate the data analysis process from beginning to end.
Suppose you observe very few entries from a large matrix. Can we predict the missing entries, say assuming the matrix is (approximately) low rank ? We describe a very simple method to solve this matrix completion problem. We show our method is able to recover matrices from very few entries and/or with ill conditioned matrices, where many other popular methods fail.
Universal dynamic regret is a natural metric for the performance of an online learner in nonstationary environments. The optimal dynamic regret for strongly convex and exponential concave losses, however, had been open for nearly two decades. In this talk, I will cover some recent advances on this problem from my group that largely settled this open problem.
Events at the Center for Statistics and Machine Learning are currently being scheduled. Please check back here later this month for events.
In this talk, recent results on various aspects of the Empirical Risk Minimization (ERM) problem with Relative Entropy Regularization (ERM-RER) are presented. The regularization is with respect to a sigma-finite measure, instead of a probability measure, which provides a larger flexibility for including prior knowledge on the models. Special cases of this general formulation include the ERM problem with (discrete or differential) entropy regularization and the information-risk minimization problem.