Education
One of ML@B’s core pillars is democratizing access to ML education. ML@B prioritizes open source education initiatives that provide resources and educate underrepresented communities within the Berkeley community and beyond.
Courses
We have experience developing content, teaching, and working with students on all levels. Our Education Committee (Edu) officers are passionate and experienced instructors who help shape the next generation of ML researchers and engineers. Take a look at some of our past courses below!
Deep Learning for Computer Vision
Modern computer vision is a fast-changing space with new model architectures produced every month. This course provides a survey of the recent transformative developments in the field, in object detection, feature segmentation, generative modeling, vision transformers, and 3D vision.
Recommendation Systems
In this course, you will learn how Big Tech develops content and product recommendation systems to provide customized experiences, increase engagement, and drive up customer satisfaction. We explore content-based and collaborative filtering paradigms, architectures using statistical methods, deep learning, CNNs, autoencoders, RNNs, Transformers, GANs, and deep RL. We'll also delve into scoring, re-ranking, evaluation, deployment, ethics, decision-making psychology, and adversarial attacks.
ML@B Ignite
Are you a local Bay Area high school junior or senior looking to explore machine learning for the first time? Want to learn how to build your own ML project? Join ML@B Ignite for an introduction to machine learning!
Resources
Interested in machine learning but don't know where to start? Check out some of ML@B's publicly-available, published content on various fields within machine learning.
