Week of Sept. 24
Introduction. Simulation, computation, and generative models. Probability and belief.
Homework: Excercises on Scheme Basics and Generative Models.
- Scheme Basics
- Generative Models
- How to grow a mind: structure, statistics, and abstraction. J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman (2011). Science.
- Internal physics models guide probabilistic judgments about object dynamics. Hamrick, Battaglia, Tenenbaum (2011).
- Optional: Structure and Interpretation of Computer Programs. (This is an amazing intro to computer science, through Scheme.)
- Optional: Some Scheme tutorials.
- Optional: Sources of uncertainty in intuitive physics. Smith and Vul (2012).
Week of Oct. 1
Conditioning and inference. Causal vs. statistical dependency. Discussion on levels of analysis.
Homework: Excercises on Conditioning.
- Patterns of Inference (first few sections)
- Predicting the future. Griffiths and Tenenbaum (2006).
- Chapter 1 of "The adaptive character of thought." Anderson (1990).
- Optional: Chapter 1 of "Vision." Marr (1982).
- Optional: Ten Years of Rational Analysis. Chater, Oaksford (1999).
- Optional: The Knowledge Level. Newell (1982).
- Optional: Ping Pong in Church: Productive use of concepts in human probabilistic inference. Gerstenberg and Goodman (2012).
Week of Oct. 8
Patterns of inference. Sequences of observations.
Homework: Excercises on Patterns of Inference, also work on mini-project.
Mini-project for class on thursday.
- (Remainder of) Patterns of Inference
- Models for sequences of observations
- Causal Reasoning Through Intervention. Hagmayer, Sloman, Lagnado, and Waldmann (2006).
- Optional: Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. Sobel, Tenenbaum, Gopnik (2004).
- Optional: Bayesian models of object perception. Kersten and Yuille (2003).
Week of Oct. 15
Inference algorithms and process models.
- Algorithms for Inference
- One and done: Globally optimal behavior from locally suboptimal decisions. Vul, Goodman, Griffiths, Tenenbaum (2009).
- Burn-in, bias, and the rationality of anchoring. Lieder, Griffiths, and Goodman (2012).
- Optional: Perceptual multistability as Markov chain Monte Carlo inference. Gershman, Vul, Tenenbaum (2009).
- Optional: A more rational model of categorization. Sanborn, Griffiths, Navarro (2006).
- Optional: Theory acquisition as stochastic search. Ullman, Goodman, and Tenenbaum (2010).
- Optional: Exemplar models as a mechanism for performing Bayesian inference. Shi, Griffiths, Feldman, Sanborn (2010).
Week of Oct. 22
Homework: Excercises on Inference about Inference, also work on project proposals.
- Inference about Inference
- Goal Inference as Inverse Planning. Baker, Tenenbaum, Saxe (2007).
- Optional: Cause and intent: Social reasoning in causal learning. Goodman, Baker, Tenenbaum (2009).
- Optional: Reasoning about Reasoning by Nested Conditioning: Modeling Theory of Mind with Probabilistic Programs. Stuhlmueller and Goodman (2013).
- Optional: Young children use statistical sampling to infer the preferences of other people. Kushnir, Xu, and Wellman (2010).
Week of Oct. 29
Social cognition (continued). Natural language pragmatics.
Project proposals due Friday!
- Quantifying pragmatic inference in language games. Frank and Goodman (2012).
- Optional: Teaching games: statistical sampling assumptions for learning in pedagogical situations. Shafto and Goodman (2008).
- Optional: Knowledge and implicature: Modeling language understanding as social cognition. Goodman and Stuhlmueller (2013).
- Optional: Probabilistic Semantics and Pragmatics: Uncertainty in Language and Thought. Goodman and Lassiter (Aug 2013 draft).
Week of Nov. 5
Learning as inference.
- Learning as Conditional Inference
- A rational analysis of rule-based concept learning. Goodman, Tenenbaum, Feldman, and Griffiths (2008).
- Optional: Rules and similarity in concept learning. Tenenbaum (2000).
- Optional: Learning Structured Generative Concepts. Stuhlmueller, Tenenbaum, and Goodman (2010).
Week of Nov. 12
Hierarchical models. Occam's razor.
- Hierarchical Models
- Occam's Razor
- Structure and strength in causal induction. Griffiths and Tenenbaum (2005).
- Optional: Bayesian modeling of human concept learning. Tenenbaum (1999).
- Optional: Word learning as Bayesian inference. Tenenbaum and Xu (2000).
- Optional: Word learning as Bayesian inference: Evidence from preschoolers. Xu and Tenenbaum (2005).
- Optional: Learning overhypotheses. Kemp, Perfors, and Tenenbaum (2006).
- Optional: Object name learning provides on-the-job training for attention. Smith, Jones, Landau, Gershko-Stowe, and Samuelson (2002).
Week of Nov. 19
Mixture and non-parametric models.
- Old pages on Mixture and Non-parametric models: Mixture Models, Non-Parametric Models.
- Learning systems of concepts with an infinite relational model. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., Ueda, N. (2006).
- Optional: Learning to learn causal models. Kemp, C., Goodman, N., Tenenbaum, J. (2010).
Week of Nov. 26
Week of Dec. 3
Presentations will be Dec. 3, 1:00-3:30p. Each project team will present a short summary. We'll go in alphabetical order.
Project reports are due Saturday, Dec. 7, by midnight.