AI Bootcamp X - Intro to Machine Learning
- Internal Event
Announcing the Tenth EAS AI Bootcamp: Introduction to ML
We're excited to announce that the sixth AI bootcamp is scheduled for March 31 to April 4th, 2025 in Resnick 120. This session is designed for researchers who want to grasp fundamental ML concepts and explore the potential of integrating ML into their research. There were requests from some of the graduate students and researchers to have this bootcamp during the summer so they can participate in it. We are happy to offer this repeat session to ensure more researchers can benefit.
What to Expect:
- Daily Structure: Each day will feature one to two lectures, complemented by two or more practical, hands-on sessions.
- Topics Covered: AI fundamentals, including regression, classification, clustering, embeddings, Industrial ML, and neural networks.
- Objective: Our goal is to equip you with the necessary skills to incorporate ML tools into your research and to aid your ability to explore more advanced ML techniques independently.
Joining the Bootcamp:
● Availability : Limited to 20 participants.
● Registration: Sign up using this link and complete the pre-screening Python Programming Quiz before 12 AM Pacific Time on March 27th . Please note that your enrollment won't be complete until you have taken the quiz and have received a confirmation email from the bootcamp organizers.
● ( Optional but highly recommended ) email us about yourself and your research and let us know how you think that this bootcamp can help you with your research.
Prerequisites: To maximize your learning experience, familiarity with the following is required:
● Linear Algebra: Vectors, matrices, vector spaces, matrix operations (The Matrix Cookbook), eigenvalues and eigenvectors, norms and distance metrics, linear transformation and basis. Covered in Ma1b, ACM104
● Multivariable Calculus: Partial derivatives, integration, limits, and continuity. Covered in Ma1ac
● Probability Theory: Random variables, statistical measures, probability distributions, and bayesian inference. Covered in courses such as Ma3, ACM116, ACM157, ACM 158
● Python Programming: Basic syntax and libraries. Covered in CS1. We will cover NumPy and other important libraries during the first day.
Contact:
● Bootcamp director: Reza Sadri
● Administrative assistant: Caroline Murphy
Deadline for Registration: before 12AM Pacific Time on March 27th
Computing Resources: Hands-on sessions will primarily utilize Google Colab. Should there be a need for more computational resources than the free tier of Colab provides participants can opt for Colab Pro. We will offer reimbursements for a certain number of credits. Further details will be provided on the first day of the bootcamp.