Four practical workshops introduced participants to modern machine learning tools and methods on Sunday, March 12, 2023.
1. Introduction to Graph Neural Networks and Their Generalizations
Instructor: Mustafa Hajij · University of San Francisco
Graphs represent relational data, from social networks and proteins to transportation systems. Learn fundamental GNN models, message passing and PyTorch Geometric.
2. Bayesian Methods in A/B Testing
Instructor: Nathaniel Stevens · University of Waterloo
Explore Bayesian inference, posterior probabilities, credible intervals and practical product experimentation for modern online A/B testing.
3. Introduction to Deep Learning with PyTorch
Instructor: Carlos Garcia · University of San Francisco
Build neural networks with Python and PyTorch while learning tensors, automatic differentiation, activation functions and model training.
4. Adapting Pretrained Models for Document Classification
Instructor: David Guy Brizan · University of San Francisco
A practical introduction to adapting pretrained models to applied text classification and NLP tasks.
Build, experiment, learn.
Hands-on sessions connect mathematical foundations to real-world data science workflows.
