UC Irvine Machine Learning Repository
The UC Irvine Machine Learning Repository is one of the most popular and respected resources for datasets in the field of machine learning and data science. It was created in 1987 by PhD student David Aha as an FTP archive at the University of California, Irvine, and serves to this day as a standard reference for students, academics, and machine learning practitioners.
The repository contains hundreds of datasets that cover a diverse range of domains such as biology, health and medicine, engineering, social sciences, physics and chemistry, and computer science. These datasets vary in size and complexity, making the repository useful for learning fundamental machine learning concepts and validating real-world models. Many groundbreaking machine learning papers have used UCI datasets as benchmarks for model evaluation (see Khan et al., 2018, Klambauer et al., 2017, and Rabbi et al., 2021 to see how researchers have used UCI datasets in their work).
Potential applications include:
- Academics and learning: Students can use these datasets to learn fundamental machine learning concepts like regression, classification, and clustering.
- Benchmarking algorithms: Researchers use this resource to compare new models against established datasets to create benchmarks and demonstrate methods of reproducing scientific work.
- Practical applications: Larger, domain-specific datasets (such as health or text data) are applied to real-world machine learning tasks.
Over the many years it has been available, the UCI Machine Learning Repository has remained an essential resource for anyone learning or working to advance data science by providing free, accessible, and well-documented datasets.
Probabilistic Programming & Bayesian Methods for Hackers
The free online book Probabilistic Programming & Bayesian Methods for Hackers was written by Cameron Davidson-Pilon (among other contributors), a data scientist and author known for making statistical methods approachable to a wide audience. Interestingly, this book was created using GitHub (via pull requests) and generated by Jupyter Notebook.
This book forcuses on Bayesian statistics and probablilistic programming, which are taught through practical examples written in Python that take incremental steps toward an understanding of Bayesian methods, as apposed to complex mathematical analysis. Instead of heavy mathematical derivations, it uses a hands-on style that is accessible to readers with a wide range of backgrounds, including those without a deep understanding of math and statistics. Topics in the book include Bayesian inference, Markov Chain Monte Carlo (MCMC), and real-world applications like A/B testing and survival analysis.
I am particularly interested in this book due to its unique requirements for consuming the book’s content. To read the book, users must first clone the book’s GitHub repository, install Jupyter (if the reader would like to run the provided code and try the practice questions), and download the ipynb files to their local machine. Besides the delightfully interactive requirements to read and interact with it, this book interests me because Bayesian methods are becoming increasingly important in data science for handling uncertainty in data and decision making. The book also aligns with the goals of this course by emphasizing practical, code-based learning that can be directly applied to real datasets and projects. Its open-source, interactive format makes it easy to experiment with examples and simplifies the integration of these concepts into my own work.
Web Exercise 1 Website (you are here): nthPerson.GitHub.io
Link to my GitHub account: nthPerson’s (Robert Ashe) GitHub Account