| 21 Sep | Lecture 1: Introduction
Reading: F&L ch. 1
Optional: Maths Tutorials | Lecture 2: Model Building
Reading: F&L ch. 2
Optional: CCS R Cheat Sheet | | |
| 28 Sep | Lecture 3: Parameter Estimation
Reading: F&L ch. 3 | Lecture 4: Maximum Likelihood Estimation
Reading: F&L ch. 4
Optional: Maths Tutorials | | |
| 5 Oct | Lecture 5: Bayesian Estimation 1
Reading: F&L ch. 6 | Lecture 6: Bayesian Estimation 2
Reading: F&L ch. 6 | Tutorial 1: Basic Model Building
Solutions | |
| 12 Oct | Lecture 7: Aggregation across Participants
Reading: F&L ch. 5 | Lecture 8: Model Comparison
Reading: F&L ch. 10 | Tutorial 2: Parameter Estimation
Solutions | |
| 19 Oct | Lecture 9: Concepts
Reading: Tenenbaum (2000) | Lecture 10: Categorization
Reading: Battleday et al. (2020) | Tutorial 3: Model Comparison
Solutions | |
| 26 Oct | Lecture 11: Causality 1
Reading: Baetu and Baker (2016)
Optional: Wasserman and Castro (2022) | Lecture 12: Causality 2
Reading: Tenenbaum and Griffiths (2001)
Optional: Charniak (1991), Pearl and Mackenzie (2018) | Tutorial 4: Bayesian Estimation
Solutions | Released 29 October
Materials on Learn |
| 2 Nov | Lecture 13: Active Learning
Reading: Steyvers et al. (2003) | Lecture 14: Language Acquisition 1
Reading: Frank et al. (2009) | Q&A | |
| 9 Nov | Lecture 15: Language Acquisition 2 | Lecture 16: Language Processing 1 | Tutorial 5: Active Learning
Solutions | |
| 16 Nov | Lecture 17: Language Processing 2 | Lecture 18: Large Cognitive Models 1 | Tutorial 6
Solutions | Due 19 November 12:00 noon |
| 23 Nov | Lecture 19 Large Cognitive Models 2 | Lecture 20: Recap and Q&A | Tutorial 7
Solutions | |
