This GitHub repository will contain all material covered during URI's Spring 2023 Machine Learning Workshop Series. We are excited to make the most of this opportunity to expose the community to some introductory Machine Learning tools and techniques, and hope to successfully foster an interest of these topics in all of you.
PhD Candidate, Department of Computer Science & Statistics @ University of Rhode Island
Associate Professor & Director of Graduate Studies, Department of Computer Science & Statistics @ University of Rhode Island
| Date | Title | Topics |
|---|---|---|
| February 10th | Python Tools for Machine Learning RSVP - Google Colab |
The first in a series of workshops geared toward making Machine Learning more accessible to the community. In this first workshop, we will be covering useful tools and techniques such as NumPy, Tensors, Slicing, List comprehension, and Broadcasting. This will lay the groundwork for future workshops where we will dive into more complex topics such as exploring ML frameworks, configuring data loaders, and creating & using ML models. Basic Python knowledge of variables, assignments, operators, functions, and lists is required to attend. |
| February 24th | Classes in Python & Deep Learning Frameworks RSVP - Google Colab |
In the second workshop of the semster, we will begin diving into what makes Machine Learning models work. Topics covered will include Python classes, data loaders, and the TensorFlow framework. By the end of this session attendees will understand the various components of a Neural Network and have gained experience in developing a ML model to accomplish a specific task. |
| March 24th | Fine Tuning for Downstream Tasks RSVP - Google Colab |
Building on results from the second workshop, our third will focus on transfer learning. We will explore downloading a pre-trained model, analyzing the requirements for its use, and fine tuning it for a downstream task. The differences between text and image processing will be covered, as well as venues for publishing your completed model. |
| April 21st | Reporting Your Findings in LaTeX RSVP - Google Colab |
In the fourth and final installation of this workshop series we will discuss how to properly report the results of a machine learning experiment, give a meaningful presentation of your research, write a proper report using LaTeX, and use Git as a collaboration tool. |
This workshop series is being funded by a Tensorflow and Google AI award to support machine learning courses and diversity programs.
