For in-person engagement, students will be able to come to the UC San Diego campus (Advanced) or UT Austin campus (Beginner's) to experience FinDS. The classes will be held in one of our high-tech classrooms. 

The agenda for in-person engagement will consist of 2 hours of lecture followed by problem-solving sessions each day, allowing ample opportunities for one-to-one and group discussion. 

Advanced Program students who excel will have the opportunity to engage in research projects with graduate students of UCSD after the completion of the course.

Syllabus for FinDS Beginner: 

• Proofs, Induction, Discrete Mathematics 

•  Introduction to Graph Theory and Algorithms 

• Linear Algebra and Connection to Machine Learning 

• Probability and Connection to Randomized Algorithms 

Syllabus for FinDS Advanced: 

WEEK 1: Foundations of Machine Learning

• Calculus & Gradient Descent

• Recap of some necessary ingredients of Calculus

• Gradient Descent, Stochastic Gradient Descent

Students learn about convergence, but also identify datasets to implement these algorithms.

WEEK 2: Foundations of Algorithms

• Graph Exploration

• Computing Shortest Paths & Other Graph Statistics

Students implement and experiment with the algorithms learned.

WEEK 3: Linear Algebra in Action

• Spectral Clustering

• Page Rank Algorithms