AI Club @ OSU

Meeting Slides

Catch up on our general and project meetings slides here!

General Meetings

Week 1: Introduction to AIC
Introduction to the Oregon State Artificial Intelligence Club and our officer team.
Week 2: Mathematical Foundations of AI/ML
Basics of linear algebra & statistics, Frequentist vs Bayesian methods, and formulas for MLE & MAP.
Week 3: General & Project Meeting
Review from Week 2: overview of statistics, basics of supervised learning, Frequentist vs Bayesian methods, and formulas for MLE & MAP.
Week 4: Exploring Graduate School Options in CS/AI
Overview of graduate school and basic requirements of full-time graduate studies, part-time graduate studies, and OSU's Accelerated Masters Program.
Week 8: Model Calibration and Adversarial Attacks
Overview of model calibration and adversarial attacks, and how to mitigate them

Project Workshops

Week 1: Setup & Learning New Skills
Project Workshop intro, environment setup, tutorials, and project ideation
Week 2: Problem & Dataset Selection
Problem and dataset selection with examples and dataset repositories
Week 3: Digging Into Your Data
Splitting your data, exploratory data analysis, data preprocessing, and feature engineering
Week 4: Models and Hyperparameters
Overview of different models' strengths and weaknesses, and how to tune hyperparameters
Week 5: Catch Up or Get Ahead
No new content, just an opportunity to catch up or get ahead on your project
Week 6: Final Model Selection
Selecting your final model and tuning it comprehensively
Week 7: Deployment Planning
Designing your project's deployment and choosing a tool for the job
Week 8: Deployment Implementation
Implementing your project's interface and deployment pipeline
Week 10: Wrapping Up
Wrapping up the term and collecting projects