What & Who: A series of three one-hour workshops that introduces the main concepts and techniques of machine learning, including: defining a machine learning problem;  choosing data features and an appropriate algorithm; and the challenges of overfitting and underfitting. Algorithms will include linear regression, logistic regression, k-nearest neighbors, and neural networks and deep learning. We will use Scikit-Learn for the introductory portions (days 1 and 2), Keras for neural nets and deep learning (day 3).

Participants will need a personal Google account (not their Caltech Google account). We will be using Google's Colaboratory environment for the workshops. For an introduction to Colab see: https://colab.research.google.com/notebooks/intro.ipynb

The workshops will assume familiarity with the following:

• Basic Python script structure and commands
• Using a Jupyter Notebook
• Introductory knowledge of the NumPy, pandas, and matplotlib Python libraries

A quick review of these topics will be provided at 11:45am, before the first workshop.

When: Wednesday-Friday, February 23–25, 2022.

Where: Online via Zoom

Instructors: Stephen Davison (days 1-2), Charles Guan (day 3)

Schedule:
11:45am-12:00pm: Setup, discussion, troubleshooting (as needed, optional)
12:00pm-1:15pm: Instruction (approximately 15 mins at end for optional review, discussion, troubleshooting)

Registration: This event is open to the Caltech and JPL communities  Please register with your Caltech or JPL email.

For more information: If you have any questions about the workshop please contact the Library (library@caltech.edu).