# 9.66 Computational Cognitive Science ## 📝 Course Overview This course explores how computational models can help us understand human cognition, including perception, learning, reasoning, and decision-making. ## 🎯 Learning Goals - Understand Bayesian approaches to cognition - Learn probabilistic programming - Apply computational models to cognitive phenomena ## 📚 Course Materials - [[Lecture Notes]] - [[Problem Sets]] - [[Final Project]] ## 🔗 Related - [[Papers/Cognition]] - [[Bayesian Modeling]] - [[Probabilistic Programming]] ## 📌 Key Topics 1. Bayesian inference 2. Generative models 3. Causal reasoning 4. Learning and development --- ## Weekly Notes ### Week 1: Introduction - Topic overview - Bayesian fundamentals ### Week 2: Probabilistic Models - Model building - Inference methods [... continue with weekly notes ...]