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: Wednesday-Friday, February 23–25, 2022.
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 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 (firstname.lastname@example.org).
11:45am - 1:15pm, Thursday, February 24, 2022
11:45am - 1:15pm, Friday, February 25, 2022