Introduction to Machine Learning
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: Tuesday–Thursday, June 15–17, 2021.
Where: Online via Zoom
Instructors: Stephen Davison (days 1-2), Charles Guan (day 3)
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 only open to the Caltech and JPL communities Please register with your Caltech or JPL email. Since spaces are limited and we expect the workshop to be oversubscribed, please only register if you are sure that you can attend.
For more information: If you have any questions about the workshop please contact the Library (firstname.lastname@example.org).
11:45am - 1:15pm, Wednesday, June 16, 2021
11:45am - 1:15pm, Thursday, June 17, 2021